Curriculum alignment · The course
Grade 9: units, topics and lessons
Every Grade 9 topic in the LittleAIMaster AI course, its lessons, and the requirements from 14 AI curricula it helps meet. Open a topic to see its lessons; lessons cited as evidence are in bold.
6units73topics1,520lessons229requirements met
Looking for the learner view? See the Grade 9 learning path.
Unit 1: Math for AI 12 topics
Welcome, AI Engineers!25 lessons· meets 37 requirements inUAEADEKCBSECISCE + states+8
Lessons
- The AI Revolution
- AI Engineer Revealed
- What Makes an AI Engineer?
- How Machines Learn
- Be the Model
- One Career, Many Industries
- AI Concept Match
- Why This Career Grows Fast
- Data is Everything
- Sort the Approaches!
- Three Ways to Build AI
- Which Family?
- Choose the Family
- Python Power
- Predict the Output
- Tune an Engineer's Toolkit
- Netflix Needs the Full Pipeline
- AI in Action
- AI Pipeline Order
- Who Does What in the Pipeline
- Spot the ML!
- Knowledge Check!
- Explain the Engineer Role
- AI Vocabulary
- AI Engineer Ready!
Requirements this topic helps meet
- UAEHow AI systems work; data and design choices
- ADEKCareers, future of work
- CBSEDifferentiate AI, ML, and DL and common data terminologies; familiarise with supervised, unsupervised, and reinforcement learning-based approaches; understand subcategories (classification, regression, clustering, association, ANN, CNN); understand neural networks and how AI makes a decision (Unit 2: Advanced Concepts of Modeling in AI).
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesIdentify applications and benefits of AI across commercial, industry, medical/healthcare, defence, banking, entertainment, transport, security and agriculture domains
- CISCE + statesClassify types of data (audio, visual, numeric, text) and explain how data becomes information within the AI Project Cycle
- CISCE + statesExplain the evolution of computing from deterministic (binary logic, conditional gates) to probabilistic approaches and why AI is needed for probabilistic, real-life problems
- CISCE + statesCompare automated vs autonomous systems and deterministic vs probabilistic decision making; contrast human decision making (subjective) with machine decision making (objective) via object-classification examples
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesExplain the brief history and primary elements of AI including Machine Learning (ML) vs Deep Learning (DL)/neural networks, and applications of ML/DL such as image recognition, computer vision, speech recognition and information retrieval
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- England + CambridgeUnderstand what is meant by Artificial Intelligence (AI)
- England + CambridgeDescribe the main characteristics of AI: the collection of data and rules for using that data, the ability to reason, and (optionally) the ability to learn and adapt
- England + CambridgeExplain the basic operation and components of AI systems that simulate intelligent behaviour, limited to expert systems (knowledge base, rule base, inference engine, interface) and machine learning (a program's ability to automatically adapt its own processes and/or data)
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.
- CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
- OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
- OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
- UNESCOUse examples of AI tools (facial recognition, recommenders, self-driving cars, loan-default prediction, etc.) to explain the definition and scope of AI — what AI is and is not — and identify the main categories of AI technologies in daily use (CG4.1.3.1).
- UNESCOUnderstand human roles across the AI life cycle (researchers, architecture/data engineers, beta testers, ethics/safety regulators, human-AI interface specialists, compliance auditors) and the main ethical issues in using data to train AI systems (CG4.1.3.4).
- UNESCOCritically examine, from legal, ethical and logical perspectives, whether AI should or should not be used for a given real-world challenge, and define the boundaries, goals and constraints of a problem before attempting to train an AI model (CG4.1.4.1).
- SingaporeUnderstand what AI is.
- SingaporeLearn about AI: understand what AI is and how it works.
- SingaporeDescribe Artificial Intelligence (AI) as the ability of a computer to perform complex tasks without constant human guidance and improve its performance as more data is collected.
- SingaporeGive examples of common personal and business tasks that can be performed well by AI: face recognition, voice recognition, image classification and spam filtering.
- SingaporeDefine Machine Learning (ML) as a technique used in AI and explain the difference between ML and traditional programming.
- Korea, Japan, HKJudge, based on scientific reasoning, the appropriate and effective use of information and information technology suited to purpose and situation, and consider what constitutes a desirable information society.
- ChinaStudents learn core AI concepts, theory, and application domains
- ChinaStudents learn the basic process, implementation principles and core algorithms behind AI systems
Numbers in AI21 lessons· meets 17 requirements inUAECBSECISCE + statesCSTA (US)+2
Lessons
- The Number Mystery
- Digital DNA
- How Can a Photo Be Math?
- Number Types
- Numbers Become Meaning
- Number Match!
- Why Conversion Matters
- Picture This!
- Read the Pixels
- Data Sort!
- Binary World
- Binary Decoder
- Mix a Pixel
- Photo Filters Are Arithmetic
- Feature Vectors
- Compare the Vectors
- Spot the Conversion
- Data Quiz!
- Explain the Number Trick
- Vocab Time!
- Number Master!
Requirements this topic helps meet
- UAEHow AI systems work; data and design choices
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesClassify types of data (audio, visual, numeric, text) and explain how data becomes information within the AI Project Cycle
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CSTA (US)Translate between different bit representations of real-world phenomena, such as characters, numbers, and images.
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Describe how current AI models (e.g., LLMs) use data representation.
- CSTA (US)Choose and use an appropriate representation of complex data for processing by an AI algorithm.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- AI4K12 (US)Progress from giving examples of intelligent vs. non-intelligent machines and discussing what makes a machine 'intelligent' (K-2), to demonstrating a machine perception tool (e.g. speech transcription, object recognition) and explaining why it is perception rather than mere sensing (3-5), to giving examples of specialized perception algorithms (face detection, obstacle detection, speech recognition, etc.) (6-8), to explaining how perception algorithms power real-world applications like license plate readers and Google Lens (9-12).
- AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
- AI4K12 (US)Progress from identifying the features that make objects in a collection unique and organizing them into a feature table (K-2), to constructing a feature-vector representation for a set of objects and showing that similar objects lie close together in feature space (3-5), to explaining how word embeddings represent words as numeric vectors for tasks like machine translation (6-8), to describing how a transformer network maps sequences of word-embedding vectors to output sequences (9-12).
- ChinaStudents learn core AI concepts, theory, and application domains
Mean, Median, Mode22 lessons· meets 9 requirements inCBSECISCE + statesCSTA (US)AI4K12 (US)+1
Lessons
- The 4.2-Star Mystery
- Three Ways to Say 'Typical'
- Why Doesn't 4.2 Feel Simple?
- The Mean: Add and Divide
- Practise the Mean
- One Dataset, Three Stories
- The Median: Middle Value
- Outlier-Proof?
- Why Summary Choice Matters
- The Mode: Most Frequent
- Term Match
- Pick the Right Measure
- Balance the Best Summary
- Zillow Uses the Right Center
- Where ML Uses These
- Predict the Output
- Median, Step by Step
- Is the Mean Right Here?
- Checkpoint Quiz
- Explain Which Measure Wins
- Key Terms
- Data Summariser
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- ChinaStudents learn core AI concepts, theory, and application domains
Spread and Variance22 lessons· meets 12 requirements inCBSECISCE + statesCSTA (US)AI4K12 (US)+1
Lessons
- The Weather Mystery
- Range Revealed
- Same Average, Different Risk
- Variance Explained
- Variation Changes the Story
- Variance in Action
- Compute It Yourself
- Why AI Watches Spread
- Standard Deviation
- Spread Match!
- Spread in AI
- Standardise It
- Tune the Risk Level
- Google Maps Cares About Variability
- The Bell Curve
- Use the 68-95-99.7 Rule
- Variance Sort!
- Spot the Spread!
- Variance Quiz!
- Explain Why Spread Matters
- Spread Vocabulary!
- Spread Master!
Requirements this topic helps meet
- CBSEAnalyse data (numbers/images), find patterns/relations, and understand the role of Math (statistics, linear algebra, probability, calculus) in AI; understand statistics and probability concepts and apply them to real-life scenarios such as disaster management, sports, disease prediction, weather forecast, traffic estimation (Unit 3: Math for AI).
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesGive at least one real-world application example of robots in domestic, industry, medical, defence, entertainment and agriculture domains
- CISCE + statesExplain New Age Robotic Systems (warehouse, assistant, smart-home/school/mobility robots, autonomous cars/drones, medical/healthcare robotics) and why NARS are becoming increasingly relevant and possible
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- ChinaStudents learn core AI concepts, theory, and application domains
Probability Basics21 lessons· meets 14 requirements inCBSECISCE + statesCSTA (US)AI4K12 (US)+2
Lessons
- Maya's Rainy Day
- The 0 to 1 Scale
- What Does 70% Really Mean?
- The Counting Formula
- Predict the Output
- Prediction Needs Uncertainty
- Probability Match
- Why Confidence Helps
- AI Confidence Scores
- Read the Scores
- Confidence Sorting
- Independent Events
- Independent or Dependent?
- Tune a Confidence Score
- Gmail Filters With Probability
- Probability Ranking
- Spot AI Probability
- Probability Quiz
- Explain AI Confidence
- Probability Words
- Probability Master!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesExplain the evolution of computing from deterministic (binary logic, conditional gates) to probabilistic approaches and why AI is needed for probabilistic, real-life problems
- CISCE + statesCompare automated vs autonomous systems and deterministic vs probabilistic decision making; contrast human decision making (subjective) with machine decision making (objective) via object-classification examples
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CSTA (US)Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- AI4K12 (US)Progress from describing the sounds that make up spoken language and giving example words for each vowel sound (K-2), to illustrating how sequences of sounds are recognized as candidate words despite unclear signals (3-5), to illustrating how sequences of words are recognized as phrases by resolving ambiguity with higher-level information (6-8), to illustrating the full abstraction hierarchy from waveform to sentence in speech understanding (9-12).
- Korea, Japan, HKUnderstand methods for modelling social/natural phenomena and evaluate/improve a model through simulation, having experienced solving the same problem with and without a computer.
- ChinaStudents learn core AI concepts, theory, and application domains
Conditional Probability22 lessons· meets 14 requirements inCBSECISCE + statesCSTA (US)AI4K12 (US)+2
Lessons
- The Umbrella Mystery
- Given That...
- The Extra Clue
- The Secret Symbol
- New Clues, New Odds
- Notation Match
- Context Changes Likelihood
- How It Changes
- Do the Maths
- Probability Shift
- Meet Bayes!
- Prior, Evidence or Posterior?
- Tune a Probability Update
- Netflix Updates Recommendations
- Belief Update
- Predict the Output
- AI Knows You
- Spot Conditionals!
- Checkpoint Quiz!
- Explain the Update
- New Words!
- Champion!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesExplain the evolution of computing from deterministic (binary logic, conditional gates) to probabilistic approaches and why AI is needed for probabilistic, real-life problems
- CISCE + statesCompare automated vs autonomous systems and deterministic vs probabilistic decision making; contrast human decision making (subjective) with machine decision making (objective) via object-classification examples
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- OECD–EC (Europe)Analyse how AI shapes beliefs and behaviours (Engage 1, advanced level).
- OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
- ChinaStudents learn core AI concepts, theory, and application domains
Graphs Tell Stories29 lessons· meets 19 requirements inUAEADEKCBSECISCE + states+5
Lessons
- The Hidden Message
- Why Pictures Win
- The Dashboard Secret
- The Graph Family
- Pictures Compress Patterns
- Graph Match Game
- Patterns Jump Out Visually
- X Meets Y
- Predict the Plot
- Axis Sorting
- Reading the Story
- Read the Shape
- Tune a Clearer Chart
- YouTube Studio Tells a Story
- The AI Process
- From Chart to Story
- Sort the Story
- Build It for the Principal
- New Data: The Canteen Waste Planner
- Keep or Fix?
- Pick the Chart for the Canteen
- Build the Canteen Chart
- Write the Canteen Data Story
- Spot the Graph
- Graph Master Quiz
- Explain the Graph Story
- Visualization Words
- Chart Toolkit
- Data Storyteller!
Requirements this topic helps meet
- UAEData characteristics shape AI decisions
- ADEKBuild a chart/dashboard
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEDefine data literacy and recognise its importance for informed decision-making and critical thinking; apply the Data Literacy Process Framework; differentiate Data Privacy vs Security; identify data breach risks and best practices for cyber security; determine best methods to acquire, classify, and interpret data (Unit 2: Data Literacy).
- CBSERecognise the importance of data visualisation and discover different visualisation methods; build an interactive dashboard and presentation (Unit 2 sub-topic: Project Interactive Data Dashboard).
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesRepresent explored data using bar graphs, line graphs, scatter plots and pie charts; apply modelling approaches — linear regression (prediction), rule-based, supervised learning and unsupervised learning (brief)
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesChoose and construct appropriate statistical graphs (bar graph, histogram, scatter plot, pie graph) and represent multi-dimensional data (introduction to dimensionality)
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBWhere a school offers digital design (rather than or alongside product design), demonstrate digital-tool competencies such as prototyping, graphic/web design, and multimedia editing through the same four-criterion design cycle
- CSTA (US)Create interactive data visualizations using software tools to help others better understand real-world phenomena.
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- SingaporeDevelop data competencies as one of the nine core NDLP digital competencies.
- ChinaStudents learn core AI concepts, theory, and application domains
Bar Charts and Histograms21 lessons· meets 15 requirements inADEKCBSECISCE + statesCSTA (US)+2
Lessons
- The Class Survey
- Bar Chart Revealed
- Why Are the Bars Touching?
- The Survey, Counted
- Read the Bar Chart
- Chart Shape Changes Meaning
- Histogram Discovery
- Chart Sorting
- Gaps Are Data Clues
- The Power of Bins
- Bin the Scores
- Predict the Output
- Term Match
- Choose the Right Chart
- Spotify Organizes Listening Data
- Build a Histogram
- Chart Detective
- Charts Quiz
- Explain the Chart Choice
- Chart Vocabulary
- Chart Champion!
Requirements this topic helps meet
- ADEKBuild a chart/dashboard
- CBSERecognise the importance of data visualisation and discover different visualisation methods; build an interactive dashboard and presentation (Unit 2 sub-topic: Project Interactive Data Dashboard).
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesClassify types of data (audio, visual, numeric, text) and explain how data becomes information within the AI Project Cycle
- CISCE + statesRepresent explored data using bar graphs, line graphs, scatter plots and pie charts; apply modelling approaches — linear regression (prediction), rule-based, supervised learning and unsupervised learning (brief)
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesChoose and construct appropriate statistical graphs (bar graph, histogram, scatter plot, pie graph) and represent multi-dimensional data (introduction to dimensionality)
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CSTA (US)Create interactive data visualizations using software tools to help others better understand real-world phenomena.
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- ChinaStudents learn core AI concepts, theory, and application domains
Scatter Plots22 lessons· meets 9 requirements inCBSECISCE + statesCSTA (US)AI4K12 (US)+1
Lessons
- The Grade Mystery
- Dot Discovery
- The Dot Pattern
- Two Variables
- Read the Coordinates
- Dots Reveal Hidden Links
- Match It Up!
- Each Dot Holds Two Facts
- Spot the Pattern
- Up or Down?
- Relationship Sort!
- Strengthen a Scatter Plot
- Read the Numbers
- Strava Compares Performance Signals
- Python + Features
- Read the Code
- Build the Plot
- Scatter Expert!
- Checkpoint Quiz!
- Explain the Scatter Plot
- Key Terms!
- Scatter Master!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- ChinaStudents learn core AI concepts, theory, and application domains
Correlation Basics22 lessons· meets 11 requirements inCBSECISCE + statesCSTA (US)AI4K12 (US)+1
Lessons
- The Ice Cream Mystery
- The Correlation Scale
- The Summer Connection
- Positive Correlation
- Rank the Strength
- One Number Summarizes a Pattern
- Negative Correlation
- Correlation Sort!
- Strong Does Not Mean Cause
- The Causation Trap!
- Name the Trap
- Correlation Match!
- Stress-Test a Correlation
- Zillow Screens Useful Features
- AI Feature Selection
- Predict the Output
- AI Process Order!
- Spot True Causation!
- Correlation Quiz!
- Explain Correlation vs Cause
- Vocabulary Review!
- Correlation Champion!
Requirements this topic helps meet
- CBSEAnalyse data (numbers/images), find patterns/relations, and understand the role of Math (statistics, linear algebra, probability, calculus) in AI; understand statistics and probability concepts and apply them to real-life scenarios such as disaster management, sports, disease prediction, weather forecast, traffic estimation (Unit 3: Math for AI).
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Demonstrate ways a given algorithm applies to problems across disciplines.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- ChinaStudents learn core AI concepts, theory, and application domains
Math in Python23 lessons· meets 12 requirements inCBSECISCE + statesIBEngland + Cambridge+3
Lessons
- The Math Machine
- Calculator Mode!
- The Faster Calculator
- Variable Vault
- Code Turns Math Into a Tool
- Operator Match!
- Predict the Output
- NumPy Power!
- List Magic
- Index and Length
- Code Sorting!
- Tune a Python Workflow
- NumPy Powers Real Analysis
- Stats Trio
- Stats by Hand
- Print Power!
- What Prints?
- Code Steps!
- Python Detective!
- Knowledge Quiz!
- Explain Python Math
- Python Words!
- Math Master!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBWrite programs in a chosen language (Java or Python, selected at school/department level) that implement given algorithms
- England + CambridgeDescribe the operation of a compiler and an interpreter, including how high-level language is translated and how errors are reported, and explain the role of an IDE and its common functions
- CSTA (US)Compare multiple programming languages and discuss how their features make them suitable for solving different types of problems.
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- ChinaStudents learn core AI concepts, theory, and application domains
Unit Review - Math for AI Mastery21 lessons· meets 9 requirements inCBSECISCE + statesCSTA (US)AI4K12 (US)+1
Lessons
- Victory Lap!
- Unit 1 Journey
- The Review Shortcut
- Stats Showdown!
- Many Skills, One System
- The Big 9 Ideas!
- Topic Sorting!
- Connections Beat Memorization
- Formula Flashback!
- Compute It!
- Data Pipeline!
- Balance the Review
- Duolingo-Style Review Loops
- Python Match!
- Predict the Output!
- Ultimate Challenge!
- Unit 1 Final Quiz!
- Pick the Right Tool!
- Explain the Big Picture
- Master Vocabulary!
- Math Master Champion!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- ChinaStudents learn core AI concepts, theory, and application domains
Unit 2: Supervised Learning 12 topics
ML Workflow Overview24 lessons· meets 14 requirements inCBSECISCE + statesIBCSTA (US)+4
Lessons
- The AI Recipe
- The 7 Steps
- The Missing Step
- Define the Problem
- Trainable or Too Vague?
- AI Is a Process, Not a Trick
- Problem Match
- Why Monitoring Is a Step
- Collect the Data
- Ready or Needs Fixing?
- Choose the Model
- Which Family?
- Balance an ML Workflow
- Spotify Runs a Pipeline
- Train & Evaluate
- Build the Workflow
- Check Your Workflow
- Read the Code
- Workflow Order
- Spot the Step
- Workflow Quiz
- Explain the Workflow
- Workflow Words
- Workflow Master!
Requirements this topic helps meet
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesIdentify applications and benefits of AI across commercial, industry, medical/healthcare, defence, banking, entertainment, transport, security and agriculture domains
- CISCE + statesExplain Machine Learning basics (role of data/information, importance of programming and algorithms) using an example such as fruit sorting; identify cybersecurity/ethical issues (unauthorized hardware/software use, data theft, hacking, viruses/sabotage)
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- AI4K12 (US)Progress from explaining how reinforcement learning lets a computer learn from trial-and-error experience with a reward signal, e.g. a grid-world navigation demo (3-5), to explaining the difference between supervised learning (told the correct output) and reinforcement learning (told only how well it's doing) (6-8), to selecting the appropriate learning-algorithm family (supervised, unsupervised, or reinforcement) for a given reasoning problem (9-12). K-2 is marked N/A.
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
- UNESCODevelop an age-appropriate understanding of the three types of AI algorithms — supervised learning, unsupervised learning, and reinforcement learning — and how the data behind them is acquired and labelled (CG4.1.3.2).
- SingaporeGive examples of common personal and business tasks that can be performed well by AI: face recognition, voice recognition, image classification and spam filtering.
Classification Problems21 lessons· meets 9 requirements inCBSECISCE + statesIBCSTA (US)+2
Lessons
- The Inbox That Sorted Itself
- Labels, Not Numbers
- The Inbox Clue
- Two or Many?
- Labels Let AI Decide Fast
- Match the Label List
- Probabilities Hide Behind Labels
- AI All Around
- Category vs Number
- Inside a Spam Classifier
- Run the Numbers
- Tune a Classifier
- Gmail Sorts Your Inbox
- Step by Step
- Be the Classifier
- Classification Detective
- Classification Quiz
- Explain Classification
- Name the Parts
- Class Vocabulary
- Classification Pro!
Requirements this topic helps meet
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
Regression Problems21 lessons· meets 9 requirements inCBSECISCE + statesIBCSTA (US)+2
Lessons
- The Price Guesser
- Number Prediction
- The Fair Price Question
- Numbers Everywhere
- A Line Can Predict the Future
- Prediction Match
- Numbers Need Patterns Too
- Finding the Line
- Use the Line
- Category or Number?
- Regression Types
- Line or Curve?
- Tune a Regression Model
- Uber Estimates Ride Cost
- Build Regression
- Regression Detective
- Regression Quiz
- Regression Toolkit
- Explain Regression
- Regression Words
- Regression Pro!
Requirements this topic helps meet
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
Features and Labels21 lessons· meets 10 requirements inCBSECISCE + statesIBCSTA (US)+2
Lessons
- The Recipe for Predictions
- What Are Features?
- The Missing Column
- Labels: Your Target
- Number or Category?
- Inputs Become Predictions
- Feature-Label Pairs
- Good Data Starts With Roles
- Training Data Format
- Predict the Output
- Feature or Label?
- Labeled Data Matters
- Tune Training Readiness
- Zillow Needs the Right Columns
- Known Before the Sale?
- Data Prep Steps
- Feature Detective
- Features & Labels Quiz
- Explain Features and Labels
- Data Science Words
- Data Expert!
Requirements this topic helps meet
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- AI4K12 (US)Progress from illustrating why training a classifier for a broad concept like 'dog' requires large, diverse image data by browsing datasets such as ImageNet or Coco (3-5), to illustrating how objects in images are segmented and labeled with bounding boxes to build an object-recognition training set (6-8), to evaluating a real AI training dataset's size, acquisition/labeling method, storage cost, and time-to-produce, including where bias can enter (9-12). K-2 is marked N/A.
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
Decision Boundaries21 lessons· meets 9 requirements inCBSECISCE + statesIBCSTA (US)+2
Lessons
- Drawing the Line
- Boundary Basics
- The Invisible Divider
- A Boundary You Can Draw
- Be the Boundary
- A Line Can Act Like a Rule
- Boundary Shapes
- Position Becomes Meaning
- Linear vs Non-Linear
- Quick Check: Code and Corners
- Line or Curve?
- Too Simple vs Too Complex
- Fit Diagnosis
- Tune a Boundary
- Banks Use Boundaries Too
- Finding Good Boundaries
- Boundary Detective
- Boundary Quiz
- Explain Decision Boundaries
- Boundary Terms
- Boundary Master!
Requirements this topic helps meet
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
K-Nearest Neighbors22 lessons· meets 16 requirements inCBSECISCE + statesIBEngland + Cambridge+5
Lessons
- You Are Your Neighbors
- Neighbor Voting
- The Tie Vote
- Same Neighbours, Different K
- KNN Step-by-Step
- Do the Distance Maths
- Similarity Can Be Enough
- The Power of K
- Local Neighborhoods Shape Decisions
- KNN Everywhere
- KNN Trade-offs
- Choosing K Wisely
- Tune K on Real Flowers
- KNN in Python: Predict the Output
- Tune a KNN Vote
- Netflix Finds Taste Neighbors
- KNN Algorithm Steps
- KNN Detective
- KNN Quiz
- Explain KNN
- KNN Vocabulary
- Neighbor Expert!
Requirements this topic helps meet
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- England + CambridgeYoung people should understand how AI works, its capabilities and limitations, and learn to use it effectively without becoming dependent on it; this should be delivered primarily through Computing rather than assumed to develop automatically
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- OECD–EC (Europe)Assess an AI system's strengths and limitations considering its purpose and potential impacts (Shape 1, advanced).
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
- OECD–EC (Europe)States that a foundational understanding of AI's technical functioning and the ability to recognise chances, limits and risks are indispensable for students to use AI responsibly and reflectively ('digital mündig'); calls on the Länder to integrate AI-related informatics competences and 'Bildung in der digitalen Welt' competences into curricula for all subjects and school types, without itself specifying discrete grade-level student outcomes.
- UNESCOAcquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).
- SingaporeUnderstand AI's uses, risks and limitations, and apply ethical considerations (e.g. academic integrity, proper data handling) when using AI to support learning.
- SingaporeIdentify and explain AI's potential benefits, limitations and risks.
- SingaporeUse the nearest neighbour method for a classification task with two quantitative features to demonstrate the basic principles behind ML.
Decision Trees in ML22 lessons· meets 15 requirements inUAECBSECISCE + statesIB+4
Lessons
- 20 Questions
- Tree Parts
- Be the Tree
- The Perfect Question
- Choosing the Best Question
- Rate the Split
- Questions Can Become a Model
- Tree Components
- Interpretability Is a Feature
- Trees Everywhere
- Tree Trade-offs
- Pruning Trees
- Grow, Then Prune
- Fix the Tree
- Tune a Decision Tree
- Loan Decisions Need Explanations
- Building a Tree
- Split Detective
- Decision Tree Quiz
- Explain Decision Trees
- Tree Vocabulary
- Tree Master!
Requirements this topic helps meet
- UAEEffectiveness of AI governance
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- CSTA (US)Use lists to simplify solutions, generalizing computational problems instead of repeatedly using simple variables.
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
- AI4K12 (US)Progress from giving examples of symbols encountered in daily life such as restroom icons and card suits (K-2), to explaining hierarchical tree structures such as ancestry trees and taxonomies (3-5), to translating a structure like a game board, road map, or mind map into a labeled graph of nodes and links (6-8), to translating the premises of a syllogism from English into predicate-logic notation and completing the inference (9-12).
- AI4K12 (US)Progress from drawing a tree by repeatedly splitting branches and placing data at nodes (K-2), to describing the parts of a tree (nodes, parent, leaves) and how they relate (3-5), to describing the parts of a labeled graph (nodes, links, source/target) and how trees are a special case of graphs (6-8), to describing how schemas structure information about people, places, or things within knowledge graphs used by search engines (9-12).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
- SingaporeUse the nearest neighbour method for a classification task with two quantitative features to demonstrate the basic principles behind ML.
Linear Regression21 lessons· meets 9 requirements inCBSECISCE + statesIBCSTA (US)+2
Lessons
- The Growth Chart
- The Line Equation
- Which Line Is Best?
- Best-Fit Line
- Which Line Wins?
- A Simple Line Can Predict a Lot
- Regression Parts
- Slope Tells a Story
- Regression Everywhere
- Code to Output
- When Lines Work
- Measuring Success
- Trust the Model?
- Tune a Regression Line
- Growth Charts Use Trends
- Regression Steps
- Regression Detective
- Regression Quiz
- Explain Linear Regression
- Regression Words
- Regression Pro!
Requirements this topic helps meet
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
Model Evaluation Basics21 lessons· meets 11 requirements inCBSECISCE + statesIBCSTA (US)+2
Lessons
- Test Before Launch
- The Data Split
- Perfect in Practice, Bad in Reality?
- Evaluation Workflow
- Score the Test Set
- Testing on New Data Changes Everything
- Evaluation Concepts
- Accuracy Is Only a First Clue
- Beyond Accuracy
- Beat the Baseline
- Model Health Check
- The Goldilocks Zone
- Pick the Fix
- Tune Evaluation Balance
- Inbox Filters Need Real Testing
- Evaluation Steps
- Evaluation Detective
- Evaluation Quiz
- Explain Model Evaluation
- Evaluation Words
- Evaluation Expert!
Requirements this topic helps meet
- CBSEUnderstand types of modeling (rule-based and learning-based); understand evaluation terms (true/false positive/negative); apply knowledge of deployment in future AI projects; reflect on ethical issues around AI (Moral Machine activity); gain awareness of AI bias and AI access; analyse advantages and disadvantages of AI.
- CBSEUnderstand the role of evaluation in AI systems; understand train-test split for evaluating a machine learning algorithm's performance; understand accuracy and error; learn evaluation metrics for classification (accuracy, precision, recall, F1 score) and build a confusion matrix; understand ethical concerns around model evaluation (bias, transparency) (Unit 3: Evaluating Models).
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
Accuracy and Errors22 lessons· meets 11 requirements inCBSECISCE + statesIBCSTA (US)+2
Lessons
- Two Kinds of Wrong
- The Four Outcomes
- Same Accuracy, Different Danger
- Error Consequences
- False Alarm or Miss?
- One Score Can Hide Opposite Risks
- Match the Errors
- Mistakes Cost Different Amounts
- Precision & Recall
- Do the Maths
- Precision or Recall?
- The F1 Score
- The 90% Model That Finds No One
- Formula Match
- Balance the Metrics
- Spam Filters Favor Precision
- Evaluation Flow
- Metric Detective
- Errors Quiz
- Explain Precision and Recall
- Error Vocabulary
- Error Expert!
Requirements this topic helps meet
- CBSEUnderstand types of modeling (rule-based and learning-based); understand evaluation terms (true/false positive/negative); apply knowledge of deployment in future AI projects; reflect on ethical issues around AI (Moral Machine activity); gain awareness of AI bias and AI access; analyse advantages and disadvantages of AI.
- CBSEUnderstand the role of evaluation in AI systems; understand train-test split for evaluating a machine learning algorithm's performance; understand accuracy and error; learn evaluation metrics for classification (accuracy, precision, recall, F1 score) and build a confusion matrix; understand ethical concerns around model evaluation (bias, transparency) (Unit 3: Evaluating Models).
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
Confusion Matrix22 lessons· meets 11 requirements inCBSECISCE + statesIBCSTA (US)+2
Lessons
- The Scoreboard for AI
- The Four Cells
- Where Is the Model Getting Confused?
- Reading the Matrix
- Which Cell?
- One Grid Can Explain a Whole Model
- Matrix Anatomy
- The Diagonal Tells a Quick Story
- Matrix to Metrics
- Predict the Output
- Matrix Facts
- Multi-Class Matrices
- Your Model's Own Matrix
- Diagnose the Matrix
- Move the Cells, Watch F1
- Photo Taggers Check Their Confusions
- Build the Matrix
- Matrix Detective
- Matrix Quiz
- Explain a Confusion Matrix
- Matrix Vocabulary
- Matrix Master!
Requirements this topic helps meet
- CBSEUnderstand types of modeling (rule-based and learning-based); understand evaluation terms (true/false positive/negative); apply knowledge of deployment in future AI projects; reflect on ethical issues around AI (Moral Machine activity); gain awareness of AI bias and AI access; analyse advantages and disadvantages of AI.
- CBSEUnderstand the role of evaluation in AI systems; understand train-test split for evaluating a machine learning algorithm's performance; understand accuracy and error; learn evaluation metrics for classification (accuracy, precision, recall, F1 score) and build a confusion matrix; understand ethical concerns around model evaluation (bias, transparency) (Unit 3: Evaluating Models).
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
Unit 2 Review - Supervised Learning21 lessons· meets 10 requirements inCBSECISCE + statesIBCSTA (US)+2
Lessons
- Journey Complete!
- Key Concepts Recap
- Why Did the Great Model Collapse?
- The ML Pipeline
- Read the Split
- One Workflow Connects the Whole Unit
- Match Unit 2 Concepts
- One Review Reveals the Whole System
- Algorithm Selection
- Classification or Regression?
- Pick the Algorithm
- Metrics Deep Dive
- Compute the Metrics
- Strengthen the Pipeline
- Recommendation Systems Depend on Evaluation
- Build the Pipeline
- Master Challenge
- Unit 2 Final Quiz
- Explain the Supervised Pipeline
- Unit 2 Vocabulary
- Supervised Learning Expert!
Requirements this topic helps meet
- CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
- AI4K12 (US)Progress from describing and giving examples of how people vs. computers learn (K-2), to differentiating human flexible learning from computer pattern-finding/trial-and-error learning (3-5), to contrasting human learning characteristics with narrowly-defined machine learning problems (6-8), to defining supervised, unsupervised, and reinforcement learning and giving human-learning analogues for each (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
Unit 3: Data Science Basics 12 topics
Introduction to Pandas21 lessons· meets 12 requirements inADEKCBSECISCE + statesIB+3
Lessons
- Maya's Data Disaster
- Why Pandas is Essential
- How Do You Control 10,000 Rows?
- Meet the DataFrame
- Row, Column or Cell?
- A Table in Code Can Feel Like a Superpower
- Pandas Terms Match
- One Column Can Change All at Once
- Series: One Column at a Time
- Predict the Output
- DataFrame or Series?
- Essential Pandas Commands
- Which Command Answers Maya's Question?
- How Many Hours Does One Line Save?
- Streaming Data Lives in Tables
- Pandas Workflow Order
- Spot the Pandas Power
- Pandas Fundamentals Quiz
- Explain DataFrames and Series
- Pandas Vocabulary
- Pandas Pioneer!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
Loading Data21 lessons· meets 17 requirements inADEKCBSECISCE + statesIB+4
Lessons
- Data Everywhere!
- Data Formats Decoded
- Why Won't the File Open?
- Reading CSV Files
- Read the Output
- One Library Can Read So Many Worlds
- Match the Read Function
- Loading Is a Translation Step
- Essential Parameters
- Parameter Purpose Sort
- Diagnose the Load
- File Paths Explained
- Which Kind of Path?
- Tune the Load Setup
- Spreadsheets Become Analysis Fast
- Loading Data Workflow
- Spot the Loading Error
- Loading Data Quiz
- Explain Data Loading
- Loading Data Terms
- Data Loader Champion!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEExtend Python programming skills as Unit 7 (Advance Python, 10 practical hours), building on Class IX Python basics; exact sub-unit content not extracted from the source PDF pages read.
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesUse Python packages/libraries (NumPy, Pandas, SciPy, Matplotlib), lists, tuples and string operations/built-in string functions in programs
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Evaluate the tradeoffs in how data elements are organized and where data is stored.
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Korea, Japan, HKUnderstand and apply methods for representing/storing data and for collecting, organising and analysing data (comparison, relation, change, classification), selecting, executing, evaluating and improving the analysis method.
- Australia + NZAnalyse and visualise data using structured query language (or equivalent) to interrogate relational databases
Exploring Data21 lessons· meets 12 requirements inADEKCBSECISCE + statesIB+3
Lessons
- The Mystery Dataset
- Your EDA Toolkit
- What Is Hiding in the Dataset?
- Peeking at Data
- Predict the Output
- A Few Commands Can Reveal a Lot
- Match Commands
- EDA Prevents Fast Mistakes
- Power of df.info()
- Guess the dtype
- Sort the Info
- Understanding describe()
- Read describe()
- Boost Exploration Readiness
- Notebook Analysts Explore First
- EDA Workflow Order
- Spot Data Issues
- Exploring Data Quiz
- Explain EDA
- EDA Vocabulary
- Data Explorer!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
Finding the Needle21 lessons· meets 12 requirements inADEKCBSECISCE + statesIB+3
Lessons
- Finding the Needle
- Selection Methods
- Why Did the Wrong Rows Appear?
- Selecting Columns
- Predict the Output
- Precise Selection Makes Big Tables Usable
- Match Selection Syntax
- Brackets Change the Shape
- loc vs iloc
- loc or iloc?
- Code to Output
- Advanced Tricks
- What Comes Back?
- How Many Rows Does a Slice Return?
- Analysts Slice Tables All Day
- Build the Selection
- Spot Selection Errors
- Selecting Data Quiz
- Explain Data Selection
- Selection Vocabulary
- Selection Master!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
The Search Mission21 lessons· meets 14 requirements inADEKCBSECISCE + statesIB+4
Lessons
- The Search Mission
- How Filtering Works
- How Did Thousands of Rows Shrink So Fast?
- Basic Filtering Syntax
- Read the Mask
- A Question Becomes a Search Tool
- Match the Filter
- True and False Build the Answer
- Combining Conditions
- AND or OR?
- Kept or Dropped?
- Special Filtering Methods
- Pick the Method
- How Many Rows Survive?
- Stores Filter Orders Constantly
- Build the Filter
- Spot Filter Bugs
- Filtering Data Quiz
- Explain Filtering
- Filtering Vocabulary
- Filter Champion!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Evaluate the tradeoffs in how data elements are organized and where data is stored.
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Australia + NZAnalyse and visualise data using structured query language (or equivalent) to interrogate relational databases
The Mysterious Gaps21 lessons· meets 12 requirements inADEKCBSECISCE + statesIB+3
Lessons
- The Mysterious Gaps
- Why Data Goes Missing
- Are the Gaps Dangerous?
- Detecting Missing Values
- Missing Data Still Tells a Story
- Match Detection Methods
- Dropping Can Be More Dangerous Than Missing
- Dropping Missing Data
- Predict dropna()
- Drop or Fill?
- Filling Strategies
- Compute the Fill
- How Many Rows Survive dropna()?
- Wearables Often Have Sensor Gaps
- Choose the Right Fill
- Spot the Strategy
- Write the Cleanup
- Missing Data Quiz
- Explain Missing Data Handling
- Missing Data Vocabulary
- Missing Data Master!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
The Messy Data Monster21 lessons· meets 12 requirements inADEKCBSECISCE + statesIB+3
Lessons
- The Messy Data Monster
- Common Data Problems
- Why Did the Country Count Break?
- Removing Duplicates
- Predict the Output
- Tiny Fixes Can Rescue a Whole Analysis
- Cleaning Method Match
- Invisible Errors Count Too
- Cleaning Text Data
- Code to Output
- String Method Sort
- Fixing Data Types
- Will It Convert?
- Tune the Cleaning Plan
- Customer Tables Need Cleanup
- Clean This Price!
- Spot the Clean
- Data Cleaning Quiz
- Explain Data Cleaning
- Data Cleaning Terms
- Data Cleaning Pro!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
The Hidden Information21 lessons· meets 15 requirements inADEKCBSECISCE + statesIB+4
Lessons
- The Hidden Information
- Why Features Matter
- Where Did the Weekend Signal Come From?
- Creating New Columns
- Predict the New Column
- New Features Can Reveal Hidden Structure
- Match the Feature
- Better Columns Beat Bigger Models
- Date Feature Extraction
- Read the Date
- Date or Math Feature?
- Advanced Techniques
- Bins and Dummies
- Strengthen the Features
- Delivery Apps Engineer Time Signals
- Build Age Groups
- Spot Good Features
- Feature Engineering Quiz
- Explain Feature Engineering
- Feature Engineering Terms
- Feature Engineer!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesClassify types of data (audio, visual, numeric, text) and explain how data becomes information within the AI Project Cycle
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- AI4K12 (US)Progress from describing what an intelligent agent must 'know' to make sense of a spoken question (K-2), to demonstrating how a text-to-speech/speech-recognition system resolves ambiguity using statistical context (3-5), to classifying an image scene type and describing the domain knowledge needed to understand it (6-8), to analyzing an online image dataset (e.g. ImageNet, Coco) to describe how statistical domain knowledge is derived for a computer vision system (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
A Picture Worth 1000 Rows21 lessons· meets 17 requirements inADEKCBSECISCE + statesIB+4
Lessons
- A Picture Worth 1000 Rows
- Why Visualize Data?
- Why Did the Pattern Suddenly Appear?
- Matplotlib Basics
- Predict the Picture
- A Chart Can Summarize Thousands of Rows
- Chart Type Match
- The Wrong Chart Can Hide the Truth
- Line and Bar Charts
- Scatter and Histogram
- Question to Code
- Which Chart to Use?
- Tune the Chart Choice
- Music Dashboards Depend on Charts
- Chart Customization
- What Does This Line Change?
- Spot the Right Chart
- Visualization Quiz
- Explain Data Visualization
- Visualization Terms
- Visualization Master!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSERecognise the importance of data visualisation and discover different visualisation methods; build an interactive dashboard and presentation (Unit 2 sub-topic: Project Interactive Data Dashboard).
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesRepresent explored data using bar graphs, line graphs, scatter plots and pie charts; apply modelling approaches — linear regression (prediction), rule-based, supervised learning and unsupervised learning (brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesChoose and construct appropriate statistical graphs (bar graph, histogram, scatter plot, pie graph) and represent multi-dimensional data (introduction to dimensionality)
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Create interactive data visualizations using software tools to help others better understand real-world phenomena.
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
Matplotlib's Stylish Sibling21 lessons· meets 12 requirements inADEKCBSECISCE + statesIB+3
Lessons
- Matplotlib's Stylish Sibling
- What Makes Seaborn Special
- Where Did the Labels Come From?
- Importing Seaborn
- Read the Table First
- Match the Plot Types
- The Colour Scale Is a Choice
- Histograms with histplot()
- Box Plots for Outliers
- Outlier or Not?
- Order the First Look at a Dataset
- Move the Fence
- Notebooks Open With the Same Four Plots
- Correlation Heatmaps, Step by Step
- Read the Heatmap
- Pick the Plot
- Spot the Correct Code
- Seaborn Knowledge Check
- Explain Your First Plot
- Seaborn Vocabulary
- Seaborn for ML Complete!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
The Data Detective21 lessons· meets 12 requirements inADEKCBSECISCE + statesIB+3
Lessons
- The Data Detective
- Why EDA Matters
- How Do You Read a Giant Dataset Fast?
- Quick Data Peek
- A Few Checks Can Save a Whole Project
- Match EDA Commands
- EDA Is Fast Thinking, Not Slow Browsing
- Info and Describe
- Read the Output
- Finding Missing Data
- Which Command Answers It?
- EDA Workflow Order
- How Much Is Missing?
- Analytics Teams Investigate First
- Visual Exploration
- Pick the Plot
- Spot the Right Line
- EDA Knowledge Check
- Explain EDA Workflow
- EDA Vocabulary
- EDA Master!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
Your Data Science Journey21 lessons· meets 12 requirements inADEKCBSECISCE + statesIB+3
Lessons
- Your Data Science Journey
- Pandas Foundations
- Read the Summary
- Data Selection Match
- Predict the Output: Selection
- Data Cleaning Recap
- Order the Cleaning Code
- Fill, Drop or Dedupe?
- Feature Engineering Match
- Spot the Good Feature
- Visualization Recap
- Chart Type Sort
- Chart Code to Question
- How Far Does One Fill Value Drag the Mean?
- Spreadsheet Teams Run the Same Workflow
- EDA Workflow Order
- Unit 3 Final Quiz
- Explain the Data Workflow
- Unit 3 Vocabulary
- What You Did
- Unit 3 Complete!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
Unit 4: Training Models 12 topics
The ML Toolkit19 lessons· meets 8 requirements inCBSECSTA (US)AI4K12 (US)UNESCO
Lessons
- The ML Toolkit
- What Sklearn Offers
- How Can One Library Handle So Many ML Jobs?
- The fit-predict Pattern
- One Interface Makes Model Comparison Possible
- Order the Pattern
- Match the Algorithms
- Sometimes Changing the Model Is Just One Line
- Categories vs Numbers
- Import Patterns
- Predict the Output
- What score() Computes
- Spam Filters Follow the Same Shape
- Spot Correct Import
- Which Module?
- Scikit-Learn Quiz
- Explain the Sklearn Workflow
- Sklearn Vocabulary
- Toolkit in Hand
Requirements this topic helps meet
- CBSEDefine Statistical Data and its applications; define No-Code and Low-Code AI and identify differences from coded AI; use a no-code tool (Orange Data Mining) to perform data exploration, modeling, and evaluation, relating stages to the AI project cycle (Unit 4: Statistical Data, assessed via practicals only).
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
- AI4K12 (US)Progress from explaining how reinforcement learning lets a computer learn from trial-and-error experience with a reward signal, e.g. a grid-world navigation demo (3-5), to explaining the difference between supervised learning (told the correct output) and reinforcement learning (told only how well it's doing) (6-8), to selecting the appropriate learning-algorithm family (supervised, unsupervised, or reinforcement) for a given reasoning problem (9-12). K-2 is marked N/A.
- UNESCODevelop an age-appropriate understanding of the three types of AI algorithms — supervised learning, unsupervised learning, and reinforcement learning — and how the data behind them is acquired and labelled (CG4.1.3.2).
- UNESCOAcquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).
The Memorization Trap19 lessons· meets 5 requirements inCBSECSTA (US)AI4K12 (US)
Lessons
- The Memorization Trap
- Memorising vs Learning
- Why Did the Perfect Score Collapse?
- Using train_test_split
- Predict the Output
- Honest Check or Fake Score?
- Match the Parameters
- Learned or Memorised?
- How Much to Hold Back?
- Right or Wrong?
- Order the Honest Workflow
- How Far Can a Test Score Wobble?
- Kaggle Seals the Test Answers Away
- Spot Correct Split
- Read the Scoreboard
- Train-Test Quiz
- Explain Train-Test Split
- Split Vocabulary
- The Golden Rule
Requirements this topic helps meet
- CBSEAnalyse data (numbers/images), find patterns/relations, and understand the role of Math (statistics, linear algebra, probability, calculus) in AI; understand statistics and probability concepts and apply them to real-life scenarios such as disaster management, sports, disease prediction, weather forecast, traffic estimation (Unit 3: Math for AI).
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
The Learning Moment19 lessons· meets 8 requirements inCBSECISCE + statesCSTA (US)AI4K12 (US)
Lessons
- The Learning Moment
- What Fitting Does
- What Changed Inside?
- Parameters Stored
- Happens During fit()?
- What Algorithms Learn
- Inside fit(), Step by Step
- Params vs Hyperparams
- Read the Trained Model
- Before and After Fit
- Before or After fit()?
- Be fit(): Find the Line with the Smallest Error
- Recommendation Systems Learn Adjustable Patterns
- Spot Correct Fitting
- What Does Each Line Do?
- Fitting Quiz
- Explain Model Fitting
- Fitting Vocabulary
- Fitting Master!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CISCE + statesExplain Machine Learning basics (role of data/information, importance of programming and algorithms) using an example such as fruit sorting; identify cybersecurity/ethical issues (unauthorized hardware/software use, data theft, hacking, viruses/sabotage)
- CSTA (US)Demonstrate ways a given algorithm applies to problems across disciplines.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
- AI4K12 (US)Progress from analyzing a decision-tree-building game and how the learning algorithm adds/replaces nodes (3-5), to comparing how decision-tree learning (add a node) differs from neural-network learning (adjust every weight) (6-8), to describing how decision-tree, neural-net, regression, and reinforcement-learning algorithms each adjust their own type of internal representation (9-12). K-2 is marked N/A.
Model Ready to Work19 lessons· meets 4 requirements inCBSECSTA (US)AI4K12 (US)
Lessons
- Model Ready to Work
- The predict() Method
- Predict the Output: predict()
- Probability Predictions
- Read the Probabilities
- Match Prediction Methods
- True or False: Model Outputs
- Prediction Types
- Input Must Match
- From New Student to Decision
- Acting on 51% vs 99%
- Fraud Systems Use Probability Thresholds
- Spot Correct Prediction
- Approve, Review or Block
- Prediction Quiz
- Explain Model Inference
- Prediction Vocabulary
- Transfer: A Different Model
- Prediction Master!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
The Lucky Split Problem21 lessons· meets 4 requirements inCBSECSTA (US)AI4K12 (US)
Lessons
- The Lucky Split Problem
- Cross-Validation Explained
- Read Priya's Five Scores
- K-Fold in Action
- Fold Bookkeeping
- cross_val_score()
- Shuffle Five Times
- Predict the Output
- Choosing K
- Fastest to Most Thorough
- How Loud Is Luck?
- Medical Models Need More Than One Lucky Split
- Stratified K-Fold
- Plain or Stratified?
- The Honest Workflow
- CV Code Check
- Cross-Validation Quiz
- Lucky or Legit?
- Explain Cross-Validation
- CV Vocabulary
- Chapter Complete!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
Default Settings Aren't Perfect20 lessons· meets 10 requirements inADEKCBSECSTA (US)AI4K12 (US)+1
Lessons
- Default Settings Aren't Perfect
- Parameters vs Hyperparameters
- Which Is It?
- When Is Each Number Decided?
- Learned or Set?
- Algorithm Hyperparameters
- Which Way Does the Knob Turn?
- Key Hyperparameters
- GridSearchCV Intro
- Count the Fits
- Google Tunes Its Models With a Search Service
- Grid or Random?
- GridSearchCV Code
- The Tuning Workflow
- Spot the Tuning Mistake
- Coarse, Then Fine
- Hyperparameter Quiz
- Explain Hyperparameter Tuning
- Tuning Vocabulary
- Chapter Complete!
Requirements this topic helps meet
- ADEKFootprint / resource cost of AI
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- CSTA (US)Design ways to minimize negative environmental impacts of AI and communicate those ways to others.
- CSTA (US)Students examine security implications and environmental impacts of AI systems within Systems & Security.
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
- AI4K12 (US)Progress from analyzing a decision-tree-building game and how the learning algorithm adds/replaces nodes (3-5), to comparing how decision-tree learning (add a node) differs from neural-network learning (adjust every weight) (6-8), to describing how decision-tree, neural-net, regression, and reinforcement-learning algorithms each adjust their own type of internal representation (9-12). K-2 is marked N/A.
- OECD–EC (Europe)Analyse how design and deployment decisions affect an AI system's resource consumption (Engage 5, advanced).
- OECD–EC (Europe)Consider the ethical consequences of AI systems throughout their life-cycle, including environmental impact and societal impact such as platformisation of work and low-cost labelling labour (AI example 57, competence 4.4).
The Memorization Trap19 lessons· meets 4 requirements inCBSECSTA (US)AI4K12 (US)
Lessons
- The Memorization Trap
- Memorizer vs Learner
- Read the Scores
- Diagnose From Two Scores
- Memorizing or Learning?
- Causes of Overfitting
- Cause or Red Herring?
- Fixing Overfitting
- Problem to Fix
- Catch It Before It Ships
- Leaves per Training Row
- Face ID Must Work on a Face It Has Never Seen in That Exact Pose
- Spot the Overfitting
- Which Fix Is This Line?
- Overfitting Quiz
- Better or Worse?
- Explain Overfitting
- Overfitting Vocabulary
- Chapter Complete!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
The Model That Couldn't19 lessons· meets 4 requirements inCBSECSTA (US)AI4K12 (US)
Lessons
- The Model That Couldn't
- Underfitting Explained
- Why Do Two Low Scores Still Mean Trouble?
- The Three States
- A Small Gap Is Not Always a Good Sign
- Causes of Underfitting
- Simple Models Can Fail in Quiet Ways
- Fixing Underfitting
- Underfit vs Overfit Fixes
- Fix It, Step by Step
- Boost the Model Capacity
- Loan Approval Models Need Enough Signal
- Diagnose the Model
- Predict the Output
- Underfitting Quiz
- Symptom, Cause, Fix
- Explain Underfitting
- Underfitting Vocabulary
- Chapter Complete!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
The Penalty That Helps19 lessons· meets 5 requirements inCBSECSTA (US)AI4K12 (US)
Lessons
- The Penalty That Helps
- Regularization Explained
- Score the Two Models
- Ridge (L2) Penalty
- Wild or Tame?
- Lasso (L1) Penalty
- Ridge or Lasso: True or False?
- Ridge vs Lasso
- Alpha Controls Strength
- Raise Alpha or Lower It?
- Watch a Coefficient Shrink
- Google's Click Models Keep Billions of Weights at Zero
- Spot the Mistake
- Regularization Quiz
- Tune Alpha the Honest Way
- Explain Regularization
- Regularization Vocabulary
- Transfer: A New Dataset
- Chapter Complete!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CSTA (US)Demonstrate ways a given algorithm applies to problems across disciplines.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
Which Algorithm Wins?19 lessons· meets 7 requirements inCBSECSTA (US)AI4K12 (US)Singapore
Lessons
- Which Algorithm Wins?
- How to Compare Models
- Penguin Bake-off
- Read the Leaderboard
- Beyond Accuracy
- Forest or Logistic?
- Match Algorithm to Task
- Which Factor Is Talking?
- Model Comparison Code
- Predict the Output
- The Selection Workflow
- Fair or Foul?
- Balance the Selection Tradeoffs
- Fraud Teams Compare More Than One Model
- Spot Selection Error
- Model Selection Quiz
- Explain Model Selection
- Selection Vocabulary
- Chapter Complete!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from modeling the use of a classification or search algorithm to solve a simple problem (K-2), to describing differences between decision-tree and neural-network classification algorithms (3-5), to comparing several algorithms that could solve a specific reasoning problem based on input data and decision complexity (6-8), to listing, for each of eight reasoning-problem types, an algorithm that could solve it (9-12).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
- SingaporeUse the nearest neighbour method for a classification task with two quantitative features to demonstrate the basic principles behind ML.
The Data Leakage Disaster19 lessons· meets 6 requirements inCBSECSTA (US)AI4K12 (US)Korea, Japan, HK
Lessons
- The Data Leakage Disaster
- What Leaks, and What a Pipeline Does
- Where Did the Leak Happen?
- Watch the Ruler Move
- Breaks the Rule or Follows It?
- Predict the Output
- Pipeline Code
- Leakage Often Looks Like Success at First
- Which Step Learns What?
- Nadia's Notebook, Fixed
- Pipeline Benefits
- How Far Can One Leaked Row Move the Ruler?
- Credit Models Need Leak-Free Workflows
- The 3% Lie
- Spot the Leak
- Pipeline Quiz
- Explain ML Pipelines
- Pipeline Vocabulary
- Chapter Complete!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CSTA (US)Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
- Korea, Japan, HKUnderstand methods for modelling social/natural phenomena and evaluate/improve a model through simulation, having experienced solving the same problem with and without a computer.
Unit 4 Journey Recap18 lessons· meets 4 requirements inCBSECSTA (US)AI4K12 (US)
Lessons
- Unit 4 Journey Recap
- Sklearn Essentials
- Read Maya's First Run
- CV & Tuning Review
- Would You Sign Off on This?
- Predict the Output
- Overfit vs Underfit
- Which Problem Does It Fix?
- Fix the Fitting
- Selection & Pipelines
- The Whole Workflow, in Order
- Depth and Data
- The Netflix Prize Was Decided on a Hidden Test Set
- Unit 4 Quiz
- Which Call Does the Job?
- Explain the Training Workflow
- Unit 4 Vocabulary
- Unit 4 Complete!
Requirements this topic helps meet
- CBSEExplain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).
- CBSEApply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
Unit 5: Real World ML 12 topics
The Spam Invasion24 lessons· meets 12 requirements inUAEADEKCBSECISCE + states+4
Lessons
- The Spam Invasion
- Binary Classification
- Binary, Multi-class or Regression?
- How Does AI Read Spam Without Reading Like Humans?
- Learning Phases
- Even Simple Word Counts Can Catch Massive Amounts of Spam
- CountVectorizer
- Predict the Vector
- Spam vs Ham
- Spam Detection Is Really Pattern Detection
- Spam Indicators
- Naive Bayes
- Do the Bayes Maths
- Raise the Spam Confidence
- Messaging Apps Filter Abuse Before Users See It
- The Confusion Matrix
- Match Outcomes
- Read the Matrix
- Build Pipeline
- Spot the AI
- Spam Detection Quiz
- Explain Spam Classification
- Spam Vocabulary
- Spam Detective!
Requirements this topic helps meet
- UAEData characteristics shape AI decisions
- ADEKPython analysis of a people-rating/ranking dataset
- CBSEStudy Natural Language Processing concepts and applications as Unit 6 of the Class X curriculum (20 theory + 7 practical hours); exact sub-unit learning outcomes not extracted from the source PDF pages read.
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
The Messy Text Problem19 lessons· meets 13 requirements inADEKCISCE + statesCSTA (US)AI4K12 (US)+2
Lessons
- The Messy Text Problem
- Preprocessing Steps
- Predict the Output
- Why Can't Raw Text Go Straight into the Model?
- Pipeline Order
- One Cleaning Step Can Shrink the Vocabulary a Model Has to Learn
- Stopwords Sorting
- The Right Cleaning Depends on the Task
- Stemming vs Lemmatization
- Match Word Roots
- Stemmer or Lemmatizer?
- Shrink the Vocabulary
- Search Engines Normalise Your Query Before Matching It
- Context Matters
- Would This Cleaning Step Help or Hurt?
- Preprocessing Quiz
- Explain Text Preprocessing
- Text Vocabulary
- Text Cleaner!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from demonstrating knowledge of language structure by generating plausible/implausible novel words or unscrambling sentences (K-2), to demonstrating that human language is infinite via sentence extension (3-5), to using a parser to display a sentence's syntactic (parse-tree) structure and explaining the node categories (6-8), to demonstrating how a small context-free grammar can parse or generate simple sentences, and its limits with agreement constraints (9-12).
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- OECD–EC (Europe)Weigh the benefits and disadvantages of using AI-driven search engines, including privacy and commercial-interest trade-offs (AI example 31, competence 1.1).
- OECD–EC (Europe)Be aware that AI algorithms may embody a commercial or political message rather than only serving the user's information need, with possible negative consequences such as reproducing stereotypes or spreading misinformation (AI example 48, competence 1.2).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
Numbers From Words18 lessons· meets 12 requirements inADEKCISCE + statesCSTA (US)AI4K12 (US)+2
Lessons
- Numbers From Words
- The BoW Process
- The Counting Trick
- BoW Matrix
- True or False: Bag of Words
- Match BoW Vectors
- Predict the Output
- BoW Limitations
- N-grams Sorting
- Make the Bigrams
- How Big Is the Matrix?
- Word counts still do real work in spam filters
- Sparse Matrices
- Bag of Words in Code, in Order
- Bag of Words Quiz
- Explain Bag of Words
- BoW Vocabulary
- Numbers From Words: Complete
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
- AI4K12 (US)Progress from demonstrating knowledge of language structure by generating plausible/implausible novel words or unscrambling sentences (K-2), to demonstrating that human language is infinite via sentence extension (3-5), to using a parser to display a sentence's syntactic (parse-tree) structure and explaining the node categories (6-8), to demonstrating how a small context-free grammar can parse or generate simple sentences, and its limits with agreement constraints (9-12).
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
The Word Importance Problem18 lessons· meets 12 requirements inADEKCISCE + statesCSTA (US)AI4K12 (US)+2
Lessons
- The Word Importance Problem
- TF-IDF Explained
- Which Part Is It?
- TF-IDF In Action
- Calculate It Yourself
- When Common Words Fade
- High vs Low TF-IDF
- Ranking Gets Smarter
- The Logarithm Secret
- What Moves the Score?
- BoW vs TF-IDF
- Tune a TF-IDF Score
- Rare words decide which page ranks first
- Rank the Search Results
- TF-IDF Quiz
- Explain TF-IDF
- TF-IDF Vocabulary
- TF-IDF Master!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- OECD–EC (Europe)Weigh the benefits and disadvantages of using AI-driven search engines, including privacy and commercial-interest trade-offs (AI example 31, competence 1.1).
- OECD–EC (Europe)Be aware that AI algorithms may embody a commercial or political message rather than only serving the user's information need, with possible negative consequences such as reproducing stereotypes or spreading misinformation (AI example 48, competence 1.2).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
Emotion Detection20 lessons· meets 17 requirements inUAEADEKCISCE + statesCSTA (US)+3
Lessons
- Emotion Detection
- Sentiment Indicators
- The Hidden Labels
- Sort Reviews
- Tiny Words Change Tone
- Score It, Then Break It
- Predict the Score
- Sarcasm Trap
- Will score2() Get It Right?
- Real Applications
- From Stars to Support Queue
- Beyond Text: Face, Voice, Body
- Signal or Guess?
- Emotion AI Check
- Tune a Sentiment Score
- Big review systems label emotion automatically
- Sentiment Quiz
- Explain Sentiment Analysis
- Sentiment Vocabulary
- Emotion Detective!
Requirements this topic helps meet
- UAEHow AI models human emotion and behaviour
- ADEKPython analysis of a people-rating/ranking dataset
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from giving examples of intelligent vs. non-intelligent machines and discussing what makes a machine 'intelligent' (K-2), to demonstrating a machine perception tool (e.g. speech transcription, object recognition) and explaining why it is perception rather than mere sensing (3-5), to giving examples of specialized perception algorithms (face detection, obstacle detection, speech recognition, etc.) (6-8), to explaining how perception algorithms power real-world applications like license plate readers and Google Lens (9-12).
- AI4K12 (US)Progress from describing what an intelligent agent must 'know' to make sense of a spoken question (K-2), to demonstrating how a text-to-speech/speech-recognition system resolves ambiguity using statistical context (3-5), to classifying an image scene type and describing the domain knowledge needed to understand it (6-8), to analyzing an online image dataset (e.g. ImageNet, Coco) to describe how statistical domain knowledge is derived for a computer vision system (9-12).
- AI4K12 (US)Progress from illustrating ambiguity via homophones/homonyms resolved by context (K-2), to illustrating pronoun reference ambiguity resolved by world knowledge (3-5), to illustrating prepositional-phrase attachment ambiguity resolved by meaning (6-8), to describing polysemy (multiple word senses) and how statistical NLP systems resolve it less reliably than human common sense (9-12).
- AI4K12 (US)Progress from demonstrating how a computer produces different verb tense/agreement forms (K-2), to experimenting with a speech-to-text system's ability to resolve ambiguous words via context (3-5), to illustrating how word embeddings support reasoning about word meaning, e.g. outlier detection and analogy completion (6-8), to demonstrating how a small context-free grammar parses or generates simple sentences and where it falls short, e.g. subject-verb agreement (9-12).
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from giving commonsense explanations of human behavior useful for a robot to know, e.g. choosing a plausible story ending (K-2), to explaining what everyday cultural and naive-physics knowledge a computer needs to understand a story (3-5), to explaining the naive-physics inference rules (mass, volume, conservation, forces) needed to solve a physics reasoning problem (6-8), to explaining the cultural, naive-physics, and folk-psychology knowledge needed to interpret a fable, fairytale, or Winograd-schema sentence (9-12).
- AI4K12 (US)Progress from demonstrating how computers recognize emotions in faces via mouth/eye/eyebrow shape (K-2), to illustrating how sentiment analysis judges the emotional tone of text, e.g. movie/restaurant reviews, social media (3-5), to describing how computers combine facial expression, gaze, gesture, body language, tone of voice, and word choice to recognize emotional states (6-8), to identifying ways AI applications modify their behavior in response to detected human emotional states, e.g. adaptive tutoring systems (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
Predicting Numbers18 lessons· meets 11 requirements inADEKCISCE + statesCSTA (US)AI4K12 (US)+2
Lessons
- Predicting Numbers
- Price Features
- The Price Puzzle
- Push It Up or Down?
- Run the Line
- From Labels To Numbers
- How Wrong Is It?
- Score the Predictions
- Words Into Columns
- Safe Input or Leak?
- Match Concepts
- Move the Line
- What a price model is worth when real money rides on it
- Does That Claim Hold Up?
- Regression Quiz
- Would You Believe It?
- Regression Vocabulary
- Regression, Measured
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
What Matters Most?18 lessons· meets 10 requirements inADEKCISCE + statesCSTA (US)AI4K12 (US)+2
Lessons
- What Matters Most?
- Three Ways to Measure
- The Suspicious Column
- Shuffle It and See
- Read the Run
- What the Score Can Say
- Real Effect or Noise?
- Cut the Feature List
- When a Top Feature Is a Warning
- Ship It or Check It?
- Match the Vocabulary
- Is the Drop Real?
- The shuffle test is the same tool everywhere
- Importance Quiz
- Run It Yourself
- Explain It to a Teammate
- Importance Vocabulary
- Feature Expert!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
ML's Hello World18 lessons· meets 13 requirements inADEKCISCE + statesCSTA (US)AI4K12 (US)+3
Lessons
- ML's Hello World
- Three Iris Species
- Read the Ranges
- Four Features
- The 100% That Means Nothing
- Species by Size
- Five Models, One Dataset
- Algorithm Showdown
- Train It, Then Read the Mistakes
- Predict the Output
- Six Lines, In Order
- Tune an Iris Classifier
- Naming a species from measurements — and from pixels
- Honest or Overclaimed?
- Iris Quiz
- Explain Iris Classification
- Iris Vocabulary
- Iris Master!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- UNESCOAcquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
- SingaporeUse the nearest neighbour method for a classification task with two quantitative features to demonstrate the basic principles behind ML.
The Unsinkable Ship19 lessons· meets 13 requirements inADEKCISCE + statesCSTA (US)AI4K12 (US)+2
Lessons
- The Unsinkable Ship
- Dataset Features
- Read the Data Card
- The Survival Signal
- Survival Rates
- History Becomes Data
- Handling Missing Data
- Fill the Blanks Honestly
- Number or Category?
- Feature Engineering
- Top Predictors
- A Hand-Made Scorecard
- The same job, on people who are still alive
- Ethical Questions
- Build the Titanic Pipeline
- Titanic Quiz
- Explain Titanic Modeling
- Titanic Vocabulary
- Titanic Master!
Requirements this topic helps meet
- ADEKExamine dataset bias, propose fixes
- ADEKPython analysis of a people-rating/ranking dataset
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from illustrating why training a classifier for a broad concept like 'dog' requires large, diverse image data by browsing datasets such as ImageNet or Coco (3-5), to illustrating how objects in images are segmented and labeled with bounding boxes to build an object-recognition training set (6-8), to evaluating a real AI training dataset's size, acquisition/labeling method, storage cost, and time-to-produce, including where bias can enter (9-12). K-2 is marked N/A.
- AI4K12 (US)Progress from examining a labeled dataset to identify problems that could cause incorrect predictions, e.g. an all-green 'healthy foods' set (K-2), to examining training-data features/labels for potential sources of bias such as Amazon's biased resume sorter (3-5), to explaining how unbalanced training data biases a classifier, e.g. faces or loan applications (6-8), to investigating demographic imbalances in training data using data-visualization tools such as histograms or Pandas (9-12).
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
Open Data Revolution18 lessons· meets 12 requirements inADEKCISCE + statesCSTA (US)AI4K12 (US)+2
Lessons
- Open Data Revolution
- Global Data Portals
- Which Portal, Which Question?
- From Portal to First Number
- Policy In The Data
- Why Public Files Fight Back
- Convert, Missing, or Ask?
- Open, With A Condition
- What The Number Lets You Say
- Who Isn't In The Data?
- ML Project Ideas
- How Much Survives Cleaning?
- Pick The Dataset
- A whole site built by re-publishing other people's data
- Open Data Quiz
- Explain Open Data ML
- Open Data Vocabulary
- Data Explorer!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Evaluate licenses that limit or restrict use of computational artifacts when using resources such as libraries.
- CSTA (US)Explain the beneficial and harmful effects that intellectual property laws can have on innovation.
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
The Biased Hiring Model23 lessons· meets 33 requirements inADEKCBSECISCE + statesCSTA (US)+5
Lessons
- The Biased Hiring Model
- Types of Bias
- Where Did the Bias Get In?
- The Unfair Pattern
- Real-World Bias Cases
- Accuracy Is Not Enough
- Fairness Metrics
- Read the Fairness Numbers
- Bias Hides In Proxies
- Protected Attributes
- Responsible AI Practices
- Run the Bias Audit
- Tune a Fairness Score
- High-stakes AI systems require fairness checks before deployment
- Ethics Quiz
- Stakeholder Debate: Should the Screener Stay?
- Whose Argument?
- Rebuttal Round
- Explain ML Fairness
- Fair Enough to Ship?
- Ethics Vocabulary
- One Rule For This Case
- Ethics Champion!
Requirements this topic helps meet
- ADEKFacial recognition: privacy/safety/fairness tension
- ADEKStakeholder debate / ethical matrix
- ADEKPython analysis of a people-rating/ranking dataset
- ADEKAI governance/civic participation
- CBSEUnderstand the stages of the AI Project Cycle and the three AI domains with illustrations of practical applications; learn about ethical frameworks for AI and their categories, including Bioethics as a framework used in healthcare (Unit 1).
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Test and refine computational artifacts to reduce bias and equity deficits.
- CSTA (US)Evaluate the social and economic implications of privacy in the context of safety, law, or ethics.
- CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Evaluate the design, motivation, outcomes, and potential impacts of AI systems using ethical design criteria and/or ethical frameworks.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how well an AI use aligns with ethical principles and human values across trade-offs (Engage 7, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- OECD–EC (Europe)Be aware that the data on which AI depends may include biases which can be automated and worsened by AI use (AI example 49, competence 1.2).
- OECD–EC (Europe)Recognise that AI applications directly interacting with humans and making decisions about their lives (e.g. CV-sorting, exam scoring) can be controversial even where other AI applications are not (AI example 63, competence 2.3).
- OECD–EC (Europe)Know that processing of personal data by AI (e.g. voice interactions with a virtual assistant) is subject to regulations such as the EU GDPR and can carry data protection, privacy and security risks (AI example 39, competence 4.2).
- UNESCOIllustrate dilemmas around AI and identify the main reasons behind ethical conflicts that AI creators and companies face (e.g. data collection vs. ownership, profit vs. privacy, safety vs. speed of iteration) (CG4.1.2.1).
- UNESCODiscuss age-appropriate real-world cases around six core AI ethical principles — do no harm, proportionality, non-discrimination, sustainability, human determination, and transparency/explainability — and their personal implications (CG4.1.2.2).
- UNESCOInternalise ethical principles on AI (human rights, data privacy, human agency, equity, inclusion, social justice, environmental sustainability) into reflective practices, e.g. advocating for inclusive AI interfaces and reporting discriminatory bias found in AI tools (CG4.1.2.3).
- UNESCOUnderstand human roles across the AI life cycle (researchers, architecture/data engineers, beta testers, ethics/safety regulators, human-AI interface specialists, compliance auditors) and the main ethical issues in using data to train AI systems (CG4.1.3.4).
- UNESCOEvaluate the consequences of institutional use of AI to make high-stakes decisions about people (e.g. profiling students, hiring, loan/assessment scoring) and understand why human accountability must not be ceded to AI in such decisions (CG4.2.1.2).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
- UNESCOCultivate social responsibilities to uphold 'ethics by design' in regulations on AI — evaluating how selected local AI regulations align with ethics-by-design, and drafting proposals to modify or create new regulations governing ethics by design (CG4.3.2.3).
- UNESCONurture a self-identity as a co-creator and 'driver' of the next generation of AI technology, critically analyse the long-term impacts of AI systems on social relations and behaviours, and discuss how regulations/policies should adapt to enhance AI governance (e.g. by engaging with local/online communities of AI co-creators) (CG4.3.4.3).
- SingaporeExplain how unethical use of AI or using AI with biased data can lead to negative consequences.
- Australia + NZEvaluate how the Australian Privacy Principles apply to the storage and communication of data in digital systems, and assess/manage the risks of common cyberthreats (e.g. phishing, malware) to a system
- Australia + NZRecognise how the identification and mitigation of bias is a critical aspect of machine learning, and critically evaluate the reliability, validity and ethics of AI-generated outputs and predictive strategies
- Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs
Your ML Journey19 lessons· meets 10 requirements inADEKCISCE + statesCSTA (US)AI4K12 (US)+2
Lessons
- Your ML Journey
- Complete ML Pipeline
- Order the Pipeline Code
- The 97% That Wasn't
- Text Processing Review
- Do the TF-IDF Maths
- One Unit, One System
- Algorithm Selection
- The Model Is the Small Box
- Unit 5 Chapters
- ML Pitfalls
- Diagnose the Failure
- Tune a Unit Review Score
- A recommender is a pipeline, not a single model
- Quiz: Text & Basics
- Quiz: Advanced & Ethics
- Explain The ML Workflow
- Essential Vocabulary
- Unit 5 Master!
Requirements this topic helps meet
- ADEKPython analysis of a people-rating/ranking dataset
- CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
- CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
Unit 6: ML Projects 13 topics
The Failed Project28 lessons· meets 41 requirements inUAEADEKCBSECISCE + states+8
Lessons
- The Failed Project
- 6 Stages of ML
- Why Did the Project Stall?
- Lifecycle Order
- Sofia's Two Weeks, in Code
- Tiny Decisions, Huge Difference
- SMART Framework
- Why Small Projects Win
- SMART or Not?
- Beginner Projects
- Scope Check
- Project Readiness Meter
- Planning ML Work in Trello
- 3-Week Timeline
- Week by Week
- Draw It as a Gantt Chart
- Read the Gantt
- Does This Plan Work?
- Your Own Gantt
- Team Roles, a Shared Board, a Shared Repo
- Order the Git Trace
- When the Plan Slips
- Team Mission in Repo Lab
- Common Pitfalls
- Planning Quiz
- Explain Your Project Plan
- Planning Vocabulary
- Planning Pro!
Requirements this topic helps meet
- UAEDesign AI solutions with systematic design processes
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEIdentify the AI Project Cycle framework (Problem Scoping, Data Acquisition, Data Exploration, Modeling, Evaluation, Deployment); learn problem scoping and goal-setting for an AI project; identify stakeholders; brainstorm ethical issues involved in the problem selected; identify data requirements and reliable sources.
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CBSEComplete Unit 3: Information and Communication Technology Skills (Level I-IV progressing Class IX to XII), covering topics such as email introduction and account creation, and networking basics at Class IX per secondary sources.
- CISCE + statesApply the AI Project Framework/Cycle stages — problem scoping, data acquisition, data exploration, modelling and evaluation — to a use case (in brief)
- CISCE + statesApply the 4Ws problem canvas (Who, What, Where, Why) to scope a problem and write a problem statement; identify data sources/features/datasets including the Kaggle platform for data acquisition
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- IBCollaborate with students from other Group 4 (sciences) subjects on shared scientific investigation and collaborative-sciences project skills
- IBPlan the creation of the chosen solution and follow that plan to build a prototype sufficient for testing and evaluation
- IBUse and apply technology effectively to access, process, and communicate information, and to model and create solutions to problems
- England + CambridgeDevelop capability, creativity and knowledge in computer science, digital media and information technology (statutory for all KS4 pupils, not only GCSE candidates)
- England + CambridgeDevelop and apply analytic, problem-solving, design and computational thinking skills
- CSTA (US)Distribute tasks and maintain a project timeline when collaboratively developing computational artifacts.
- CSTA (US)Design and develop computational artifacts working in team roles using collaborative tools.
- CSTA (US)Use version control systems, integrated development environments (IDEs), and collaborative tools and practices (code documentation) in a group software project.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- CSTA (US)Collaborate with many contributors through strategies such as crowdsourcing or surveys when creating a computational artifact.
- CSTA (US)Use tools and methods for collaboration on a project to increase connectivity of people in different cultures and career fields.
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- UNESCOPractise skills to identify and scope a problem that could be solved by building or training a new AI model, based on a simulation project (CG4.1.4.2).
- UNESCODevelop skills to assess an AI system's needs for data, algorithms, programming languages, software, and computing hardware, including feasibility given regulatory/ethical restrictions and cost (CG4.1.4.3).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- SingaporeKeep digital knowledge current, including awareness of emerging technologies such as AI, as one of the nine core NDLP digital competencies.
- SingaporeDevelop computational thinking as one of the nine core NDLP digital competencies.
- SingaporeDevelop digital information management skills as one of the nine core NDLP digital competencies.
- SingaporeDevelop digital communication, collaboration and participation skills as one of the nine core NDLP digital competencies.
- SingaporeApply the Find-Think-Apply-Create framework when locating, evaluating, using and creating with digital information (exact breakdown of the four components not published on the primary MOE page reviewed).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Korea, Japan, HKUnderstand the properties of information and media, and use information and information technology to discover and solve problems in the information society.
- Australia + NZPlan and manage projects individually and collaboratively, including using project management tools/strategies to sequence production activities
- Australia + NZApply digital literacy skills (investigating with, creating with, communicating and collaborating with, and managing/operating digital tools) across all learning areas, including recognising the role of AI-enabled tools in everyday digital systems
The Right Question26 lessons· meets 47 requirements inUAEADEKCBSECISCE + states+10
Lessons
- The Right Question
- X, Y, and Data
- Sort Sofia's Columns
- Why Did the Prediction Fail?
- Problem Statement Formula
- Build the Statement
- One Sentence Can Guide a Whole Model
- Good vs Bad Problems
- Bad Questions Create Bad Data
- The 3C Framework
- 3C Feature Test
- Feature Strength Meter
- Defining Targets in Spotify
- Target Leakage
- Read the Leakage Code
- Common Mistakes
- Definition Quiz
- Choose Your Own Problem
- Scope Check
- The Right Question for an AI: Prompting to Study
- Strong or Weak Study Prompt?
- Study Session in Prompt Lab
- Prompt Check
- Explain the ML Problem
- Problem Vocabulary
- Problem Definer!
Requirements this topic helps meet
- UAEApply effective prompting strategies
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEDefine data literacy and recognise its importance for informed decision-making and critical thinking; apply the Data Literacy Process Framework; differentiate Data Privacy vs Security; identify data breach risks and best practices for cyber security; determine best methods to acquire, classify, and interpret data (Unit 2: Data Literacy).
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CBSEComplete Unit 3: Information and Communication Technology Skills (Level I-IV progressing Class IX to XII), covering topics such as email introduction and account creation, and networking basics at Class IX per secondary sources.
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- IBGiven a design situation, identify a problem that needs to be solved and analyse the need for a solution
- IBConduct an inquiry into the nature of a design problem to inform the design brief
- IBWrite a detailed design specification that drives the development of a solution
- IBDevelop and present a proposed design solution derived from the specification
- IBPlan the creation of the chosen solution and follow that plan to build a prototype sufficient for testing and evaluation
- IBDesign tests to evaluate a solution, carry out those tests, and objectively evaluate the solution's success
- IBIdentify areas where a design solution could be improved and explain the solution's impact on the client or target audience
- IBUse and apply technology effectively to access, process, and communicate information, and to model and create solutions to problems
- England + CambridgeDevelop capability, creativity and knowledge in computer science, digital media and information technology (statutory for all KS4 pupils, not only GCSE candidates)
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- AI4K12 (US)Progress from describing and using AI extensions/plugins in a familiar programming framework such as Scratch, e.g. speech-to-text, face recognition (3-5), to creating a novel application using AI extensions/plugins in a chosen framework such as Scratch, MIT App Inventor, or Calypso (6-8), to creating a novel application using AI tools of the student's choice, now commonplace and freely available without advanced degrees or expensive equipment (9-12). K-2 is marked N/A.
- OECD–EC (Europe)Select and integrate outputs from multiple AI systems into a final product (Create 2, advanced).
- OECD–EC (Europe)Refine work through iterative exchanges with an AI system (Create 3, intermediate).
- OECD–EC (Europe)Know how to formulate unambiguous search queries so a conversational agent or smart speaker can respond as required (AI example 29, competence 1.1).
- OECD–EC (Europe)Weigh the benefits and disadvantages of using AI-driven search engines, including privacy and commercial-interest trade-offs (AI example 31, competence 1.1).
- OECD–EC (Europe)Be aware that AI algorithms may embody a commercial or political message rather than only serving the user's information need, with possible negative consequences such as reproducing stereotypes or spreading misinformation (AI example 48, competence 1.2).
- UNESCOInterrogate how automation of literature reviews, writing and artistic creation by AI may undermine human thinking and intellectual development, and identify concrete actions to protect themselves and peers from over-reliance on AI outputs (CG4.2.1.3).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
- UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
- UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
- UNESCOCustomize assessment methods/instruments to test self-crafted AI tools for robustness and ease of use, organise peer assessment and shared feedback, and build collaborative co-creator skills (CG4.3.3.3).
- SingaporeKeep digital knowledge current, including awareness of emerging technologies such as AI, as one of the nine core NDLP digital competencies.
- SingaporeDevelop digital information management skills as one of the nine core NDLP digital competencies.
- SingaporeDevelop digital communication, collaboration and participation skills as one of the nine core NDLP digital competencies.
- SingaporeDevelop data competencies as one of the nine core NDLP digital competencies.
- SingaporeApply the Find-Think-Apply-Create framework when locating, evaluating, using and creating with digital information (exact breakdown of the four components not published on the primary MOE page reviewed).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Korea, Japan, HKUnderstand the properties of information and media, and use information and information technology to discover and solve problems in the information society.
- ChinaStudents construct/build simple AI algorithm models and develop cross-disciplinary solutions using accumulated AI knowledge
- Australia + NZApply digital literacy skills (investigating with, creating with, communicating and collaborating with, and managing/operating digital tools) across all learning areas, including recognising the role of AI-enabled tools in everyday digital systems
The Data Hunter20 lessons· meets 28 requirements inADEKCBSECISCE + statesIB+7
Lessons
- The Data Hunter
- Free Data Sources
- Why Was the Dataset Useless?
- Sample or Skew?
- Survey Power
- A Few Good Questions Can Beat a Huge Survey
- Methods Match
- Quantity Is Not the Whole Story
- Ethics Sort
- Cleared to Use?
- Data Quantity Guide
- Audit the Export
- Dataset Quality Meter
- Collecting Responses with Google Forms
- Quality vs Quantity
- Collection Quiz
- Explain Your Data Plan
- Data Vocabulary
- Plan Your Data
- Data Hunter!
Requirements this topic helps meet
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEDefine data literacy and recognise its importance for informed decision-making and critical thinking; apply the Data Literacy Process Framework; differentiate Data Privacy vs Security; identify data breach risks and best practices for cyber security; determine best methods to acquire, classify, and interpret data (Unit 2: Data Literacy).
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Evaluate licenses that limit or restrict use of computational artifacts when using resources such as libraries.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- CSTA (US)Explain the beneficial and harmful effects that intellectual property laws can have on innovation.
- CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
- UNESCOCarry out responsible AI practices in compliance with ethical principles and locally applicable regulations, iteratively building and following a personal checklist of lawful and responsible practices (e.g. protecting privacy/copyright, marking AI-generated content, avoiding disinformation, hate speech, or sensitive content about identifiable individuals) (CG4.2.2.1).
- UNESCOGain an age-appropriate understanding of personal, legal and ethical responsibilities when using AI and reinforce self-disciplined behaviours around sensitive personal data, copyrighted materials, deepfake/identifiable-person imagery, and the spread of misinformation, disinformation or hate speech (CG4.2.2.2).
- UNESCOCategorise general safety risks of AI and typical AI incidents, deepen knowledge of human rights to data protection and privacy and creators' legal responsibility to collect data only with informed consent, and practise strategies to ensure personal data is collected, used, shared, archived and deleted only with consent (CG4.2.2.3).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- SingaporeDevelop data competencies as one of the nine core NDLP digital competencies.
- SingaporeLearn to use AI: use AI tools safely and responsibly.
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
The Messy Data Disaster20 lessons· meets 19 requirements inADEKCBSECISCE + statesIB+5
Lessons
- The Messy Data Disaster
- Cleaning Pipeline
- Why Did the Model Crash?
- Missing Value Strategies
- Predict the Output
- Cleaning Can Change the Whole Model
- Strategy Match
- Why Median Sometimes Wins
- Data Leakage Alert
- The pandas Toolkit
- Practice Sort
- Clean Data Score
- Cleaning Data in Google Sheets
- Mistake Reveals
- Build the Cleaning Script
- Preparation Quiz
- Explain a Cleaning Choice
- Prep Vocabulary
- Key Takeaways
- Data Cleaner!
Requirements this topic helps meet
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Decompose problems into smaller components through systematic analysis, using constructs such as procedures, modules, and/or objects.
- CSTA (US)Create artifacts by using procedures within a program, combinations of data and procedures, or independent but interrelated programs.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Korea, Japan, HKUnderstand means of expressing algorithms and be able to write programs (including defining/using functions) to make use of computers and networks, and evaluate/improve the resulting program.
The Magic Part20 lessons· meets 17 requirements inADEKCBSECISCE + statesIB+5
Lessons
- The Magic Part
- Baseline Model
- Why Was the Model Not Impressive?
- Algorithm Match
- A Line Can Learn Real Patterns
- Linear Regression
- Read the Output
- Why Coefficients Matter
- Training Steps
- R-Squared Explained
- Beats the Baseline?
- Model Confidence Meter
- Building Predictions in Google Maps
- Mistake Sort
- Building Quiz
- Explain the Baseline
- Model Vocabulary
- Line by Line
- Key Takeaways
- Model Builder!
Requirements this topic helps meet
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- AI4K12 (US)Progress from modeling the use of a classification or search algorithm to solve a simple problem (K-2), to describing differences between decision-tree and neural-network classification algorithms (3-5), to comparing several algorithms that could solve a specific reasoning problem based on input data and decision complexity (6-8), to listing, for each of eight reasoning-problem types, an algorithm that could solve it (9-12).
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
One Number Isn't Enough20 lessons· meets 16 requirements inADEKCBSECISCE + statesIB+5
Lessons
- One Number Isn't Enough
- R-Squared Decoded
- Why Isn't 0.746 Enough?
- Residuals, MAE and RMSE
- Which Number Answers This?
- Metrics Match
- The Shape of the Misses
- Residual Plots
- Read the Residuals
- Problem Sort
- Evaluation Thoroughness Meter
- Checking ETAs in Uber
- The Memoriser Trap
- The Evaluation Routine
- Evaluation Quiz
- Why Multiple Metrics Matter
- Evaluation Vocabulary
- Symptom to Fix
- Key Takeaways
- Evaluator!
Requirements this topic helps meet
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
Good to Great20 lessons· meets 25 requirements inADEKCBSECISCE + statesIB+6
Lessons
- Good to Great
- Feature Engineering
- Read the Rebuild
- How Did One Small Change Help So Much?
- Techniques Match
- Better Clues Can Beat Better Algorithms
- Improvement Cycle
- Most Gains Come From Understanding the Data
- Strategies Revealed
- Feature Code, Line by Line
- When to Stop
- Model Improvement Meter
- Feature Clues in Google Maps
- Smart Tips
- Real Gain or Fake Gain?
- Improvement Quiz
- Why Feature Engineering Helps So Much
- Improvement Vocabulary
- Key Takeaways
- Improver!
Requirements this topic helps meet
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Create prototypes that use algorithms to solve computational problems by leveraging prior student knowledge and personal interests.
- CSTA (US)Design and iteratively develop computational artifacts for practical intent, personal expression, or to address a societal issue by using events to initiate instructions.
- CSTA (US)Systematically design and develop programs for broad audiences by incorporating feedback from users.
- CSTA (US)Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- CSTA (US)Demonstrate ways a given algorithm applies to problems across disciplines.
- CSTA (US)Using a dataset and a systematic process, develop an AI model to generate for classification or prediction, and articulate the assumptions made at each step: (1) develop a question solvable with AI, (2) collect or curate data, (3) evaluate the data, (4) train an AI model on the data, (5) evaluate the model, and (6) iteratively improve the model.
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Korea, Japan, HKUnderstand methods for modelling social/natural phenomena and evaluate/improve a model through simulation, having experienced solving the same problem with and without a computer.
- Australia + NZStudents use an iterative process to design, develop, document, and test basic computer programs; apply design principles and usability heuristics to their own designs and evaluate user interfaces against them
- Australia + NZStudents independently design and produce a digital outcome for an authentic context, considering end-users, and evaluating the outcome's fitness for purpose
The Missing Map20 lessons· meets 25 requirements inADEKCBSECISCE + statesIB+6
Lessons
- The Missing Map
- The 6-Month Test
- Why Did a Great Project Still Feel Incomplete?
- README Structure
- Documentation Lets Code Survive Time
- Documentation Match
- Can a Reader Check That?
- The Best Comments Explain Why
- Comment Sort
- Best Practices
- Project Readiness Meter
- Documentation on GitHub
- Data Dictionary
- Fill the Dictionary
- Documentation Quiz
- Why Documentation Makes Projects Stronger
- Doc Vocabulary
- Run It From Scratch
- Key Takeaways
- Documenter!
Requirements this topic helps meet
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Evaluate licenses that limit or restrict use of computational artifacts when using resources such as libraries.
- CSTA (US)Design and develop computational artifacts working in team roles using collaborative tools.
- CSTA (US)Document design decisions using text, graphics, presentations, and/or demonstrations in the development of complex programs.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- CSTA (US)Use tools and methods for collaboration on a project to increase connectivity of people in different cultures and career fields.
- CSTA (US)Explain the beneficial and harmful effects that intellectual property laws can have on innovation.
- AI4K12 (US)Progress from drawing a tree by repeatedly splitting branches and placing data at nodes (K-2), to describing the parts of a tree (nodes, parent, leaves) and how they relate (3-5), to describing the parts of a labeled graph (nodes, links, source/target) and how trees are a special case of graphs (6-8), to describing how schemas structure information about people, places, or things within knowledge graphs used by search engines (9-12).
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- UNESCOUnderstand that human accountability is a legal obligation of AI creators and AI service providers, who are answerable for safety incidents, ethical risks in design/training, and misuses of their AI service (CG4.2.1.1).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Australia + NZPlan and manage projects individually and collaboratively, including using project management tools/strategies to sequence production activities
- Australia + NZWithin authentic contexts and taking account of end-users, students independently decompose problems into algorithms, determine when to use different control structures, and document their programs using an organised approach to testing and debugging
Science Fair Star30 lessons· meets 41 requirements inUAEADEKCBSECISCE + states+8
Lessons
- Science Fair Star
- The 5-Minute Structure
- The Numbers Behind the Pitch
- Why Did the Judges Connect So Fast?
- Problem, Quest, Result
- Story Gives Numbers a Shape
- Slide Blueprint
- Translate, Don't Downgrade
- On the Slide, or Out Loud?
- Explain Like a Pro
- Handle Questions
- Presentation Impact Meter
- One Idea Per Slide
- Good vs Bad Presenting
- Presentation Quiz
- Why the Baseline Goes Next to the Result
- Presentation Terms
- The Judge's Question
- Byte's Presentation Tips
- Your Year-End Capstone
- Build It in Python Lab
- Check Your Rows
- Read the Data Check
- Improve Once, Log It
- Step to Assumption
- My Assumption Log
- Check Your Capstone Plan
- How Your Capstone Is Judged
- Secure or Not Yet?
- Five Minutes, Handled
Requirements this topic helps meet
- UAEData characteristics shape AI decisions
- UAEDesign AI solutions with systematic design processes
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSERecognise the importance of data visualisation and discover different visualisation methods; build an interactive dashboard and presentation (Unit 2 sub-topic: Project Interactive Data Dashboard).
- CBSEComplete a Part D project/field visit/student portfolio related to Sustainable Development Goals, e.g. building an AI model with Teachable Machine or Machine Learning for Kids, or maintaining a portfolio of AI activities (4Ws canvas, system map, research on AI for SDGs).
- CBSEExtend Python programming skills as Unit 7 (Advance Python, 10 practical hours), building on Class IX Python basics; exact sub-unit content not extracted from the source PDF pages read.
- CBSEEmpower students to create positive change through AI-driven social impact projects, and equip students with skills to develop AI solutions addressing societal challenges (course objectives).
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CISCE + statesRepresent explored data using bar graphs, line graphs, scatter plots and pie charts; apply modelling approaches — linear regression (prediction), rule-based, supervised learning and unsupervised learning (brief)
- CISCE + statesUse Python packages/libraries (NumPy, Pandas, SciPy, Matplotlib), lists, tuples and string operations/built-in string functions in programs
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- IBPlan the creation of the chosen solution and follow that plan to build a prototype sufficient for testing and evaluation
- IBDesign tests to evaluate a solution, carry out those tests, and objectively evaluate the solution's success
- IBIdentify areas where a design solution could be improved and explain the solution's impact on the client or target audience
- CSTA (US)Create interactive data visualizations using software tools to help others better understand real-world phenomena.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- CSTA (US)Demonstrate ways a given algorithm applies to problems across disciplines.
- CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
- CSTA (US)Using a dataset and a systematic process, develop an AI model to generate for classification or prediction, and articulate the assumptions made at each step: (1) develop a question solvable with AI, (2) collect or curate data, (3) evaluate the data, (4) train an AI model on the data, (5) evaluate the model, and (6) iteratively improve the model.
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- AI4K12 (US)Progress from describing and using AI extensions/plugins in a familiar programming framework such as Scratch, e.g. speech-to-text, face recognition (3-5), to creating a novel application using AI extensions/plugins in a chosen framework such as Scratch, MIT App Inventor, or Calypso (6-8), to creating a novel application using AI tools of the student's choice, now commonplace and freely available without advanced degrees or expensive equipment (9-12). K-2 is marked N/A.
- AI4K12 (US)Progress from describing how AI can be used to solve a societal problem, e.g. Code.org's AI for Oceans classifying wildlife vs. debris (K-2), to designing a solution to a societal problem using AI technology, e.g. environmental protection, energy conservation (3-5), to researching a societal problem and describing how AI perception/reasoning/learning technologies could address it, referencing the UN Sustainable Development Goals (6-8), to evaluating an actual AI-for-social-good project in terms of the problem addressed and its real or potential impact (9-12).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
- UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
- UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
- UNESCOCustomize assessment methods/instruments to test self-crafted AI tools for robustness and ease of use, organise peer assessment and shared feedback, and build collaborative co-creator skills (CG4.3.3.3).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- SingaporeUse the nearest neighbour method for a classification task with two quantitative features to demonstrate the basic principles behind ML.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Korea, Japan, HKUnderstand and apply methods for representing/storing data and for collecting, organising and analysing data (comparison, relation, change, classification), selecting, executing, evaluating and improving the analysis method.
- ChinaStudents construct/build simple AI algorithm models and develop cross-disciplinary solutions using accumulated AI knowledge
- Australia + NZDevelop, modify and implement software (e.g. webpages using HTML for structure and CSS for presentation) that address defined needs, including validation and testing
The Helpful Review20 lessons· meets 27 requirements inADEKCBSECISCE + statesIB+6
Lessons
- The Helpful Review
- The Feedback Sandwich
- Why Did Good Intentions Still Hurt?
- COIN Framework
- Structured Feedback Improves Both People and Projects
- Code Review Checklist
- Fresh Eyes Catch Hidden Problems
- Good vs Bad Feedback
- Critical Errors to Catch
- Read the Review Numbers
- Tune a Review Style
- How GitHub Turns Comments Into Better Code
- Must Fix or Nice to Have?
- Receiving Feedback
- Peer Review Quiz
- Explain Why Review Structure Helps
- Catch to Consequence
- Peer Review Terms
- Byte's Review Wisdom
- You Read It Like a Reviewer
Requirements this topic helps meet
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Create prototypes that use algorithms to solve computational problems by leveraging prior student knowledge and personal interests.
- CSTA (US)Design and iteratively develop computational artifacts for practical intent, personal expression, or to address a societal issue by using events to initiate instructions.
- CSTA (US)Systematically design and develop programs for broad audiences by incorporating feedback from users.
- CSTA (US)Design and develop computational artifacts working in team roles using collaborative tools.
- CSTA (US)Document design decisions using text, graphics, presentations, and/or demonstrations in the development of complex programs.
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- CSTA (US)Use tools and methods for collaboration on a project to increase connectivity of people in different cultures and career fields.
- CSTA (US)Students read, evaluate, and critique AI-generated code as part of the Programming strand.
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Australia + NZPlan and manage projects individually and collaboratively, including using project management tools/strategies to sequence production activities
- Australia + NZWithin authentic contexts and taking account of end-users, students independently decompose problems into algorithms, determine when to use different control structures, and document their programs using an organised approach to testing and debugging
- Australia + NZStudents use an iterative process to design, develop, document, and test basic computer programs; apply design principles and usability heuristics to their own designs and evaluate user interfaces against them
- Australia + NZStudents independently design and produce a digital outcome for an authentic context, considering end-users, and evaluating the outcome's fitness for purpose
Portfolio Opens Doors20 lessons· meets 25 requirements inADEKCBSECISCE + statesIB+7
Lessons
- Portfolio Opens Doors
- Why Build a Portfolio?
- Why Did One Portfolio Stand Out Immediately?
- The 1-3-5 Rule
- A Small Portfolio Can Create Big Opportunities
- Portfolio Components
- Names and READMEs Shape First Impressions
- Naming Your Projects
- Pin or Archive?
- Making Projects Stand Out
- Tune a Portfolio Strategy
- How GitHub Helps Builders Curate Their Best Work
- README Structure
- From Output to README Line
- Portfolio Quiz
- Explain Why Curation Matters
- Portfolio Terms
- What Goes in Limitations
- Byte's Portfolio Advice
- Portfolio Curator!
Requirements this topic helps meet
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CBSEComplete Unit 3: Information and Communication Technology Skills (Level I-IV progressing Class IX to XII), covering topics such as email introduction and account creation, and networking basics at Class IX per secondary sources.
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- IBUse and apply technology effectively to access, process, and communicate information, and to model and create solutions to problems
- England + CambridgeDevelop capability, creativity and knowledge in computer science, digital media and information technology (statutory for all KS4 pupils, not only GCSE candidates)
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- SingaporeKeep digital knowledge current, including awareness of emerging technologies such as AI, as one of the nine core NDLP digital competencies.
- SingaporeDevelop digital information management skills as one of the nine core NDLP digital competencies.
- SingaporeDevelop digital communication, collaboration and participation skills as one of the nine core NDLP digital competencies.
- SingaporeApply the Find-Think-Apply-Create framework when locating, evaluating, using and creating with digital information (exact breakdown of the four components not published on the primary MOE page reviewed).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Korea, Japan, HKUnderstand the properties of information and media, and use information and information technology to discover and solve problems in the information society.
- Australia + NZApply digital literacy skills (investigating with, creating with, communicating and collaborating with, and managing/operating digital tools) across all learning areas, including recognising the role of AI-enabled tools in everyday digital systems
Last Day of Grade 911 lessons· meets 17 requirements inADEKCBSECISCE + statesIB+6
Lessons
- Last Day of Grade 9
- Your Grade 9 Journey
- One Project, Start to Finish
- Claims a Judge Can Check
- Put It in a Tester's Hands
- Read the Tester Log
- Judge It Like a User, a Community and the Planet
- Personal, Social or Planet?
- Capstone Debrief
- Year Review Quiz
- Grade 9 Complete!
Requirements this topic helps meet
- ADEKBuild a data story (data + visuals + narrative for an audience)
- CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
- CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
- CISCE + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
- CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
- CISCE + statesApply data transformation and standardization methods for analysis
- CISCE + statesDistinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)
- CISCE + statesDerive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts
- CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
- IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
- CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
- AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.
- Australia + NZDevelop, modify and implement software (e.g. webpages using HTML for structure and CSS for presentation) that address defined needs, including validation and testing
AI Careers: Who Builds, Shapes and Uses AI21 lessons· meets 5 requirements inUAEADEKUNESCOSingapore
Lessons
- The Careers Fair
- Who Built the Crop-Doctor App?
- The Builders
- Whose Task Is It?
- The Shapers
- Governments Hire AI Testers Too
- Match the Role to Its Question
- Experts Who Use AI
- AI Drafts or Expert Decides?
- A Phone That Spots Sick Cassava
- Builds, Shapes or Uses?
- Paths In: Many Doors, Few Need a PhD
- Which First Step Fits?
- Myth or Fact?
- More Jobs Created Than Lost, Still a Big Shake-Up
- Net Gain, Big Churn
- Order a Starting Path
- Checkpoint: AI Careers
- Explain One Role
- Careers Terms
- Career Explorer!
Requirements this topic helps meet
- UAEJudge responsible AI use against governance frameworks
- UAEAI in economic development and social change
- ADEKCareers, future of work
- UNESCOReflect on personal goals and a lifelong-learning, self-actualization attitude toward AI as it becomes pervasive in work, life and social practices (CG4.3.1.3).
- SingaporeLearn beyond AI: develop distinctly human capabilities and dispositions that AI cannot replicate.
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Our own reading of our own course, ratings reviewed 22 October 2026. It is not an endorsement, certification or accreditation by any authority named here, and it makes no claim about learning results.