Course Content
Lesson 1: What is AI?
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Lesson 1: What is AI?
Lesson plan – What is AI?
Slides – What is AI?
Activity – The intelligent piece of paper
Worksheet – What is AI?
Lesson 2: How computers learn from data
Learn about the role of data-driven models in AI systems and machine learning. Learning objectives: - Define machine learning’s relationship to artificial intelligence - Name the three common approaches to machine learning - Describe how classification can be solved using supervised learning Key vocabulary: Machine learning, training data, supervised learning, unsupervised learning, reinforcement learning, classification, class, label Lesson structure: - Is a ‘smart’ speaker an AI application? - Breaking down a smart speaker - How to models learn? - The types of machine learning - Classification - Classifying animals in the Serengeti
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Topic: Lesson 2: How computers learn from data
Lesson plan – How computers learn from data
Slides – How computers learn from data
Worksheet 1 – What data do you need?
Worksheet 1 answers
Activity 2 - Types of machine learning (Quiz)
Worksheet 3 – Data types for classification
Worksheet 3 answers
Lesson 3: Bias in, bias out
Create a machine learning model to classify images and explore how a limited data set can lead to bias. Learning objectives: - Describe the impact of data on the accuracy of a machine learning (ML) model - Explain the need for both training and test data - Explain how bias can influence the predictions generated by an ML model Key vocabulary: Artificial intelligence (AI), machine learning (ML), supervised learning, classification, training data, test data, accuracy, bias, data bias, societal bias Lesson structure: - The three different types of machine learning - Supermarket AI application - Training a model - Bias - Student timetable model - Reducing bias
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Topic: Lesson 3: Bias in, bias out
Lesson plan – Bias in bias out
Slides – Bias in, bias out
Worksheet 1 – Training a model
Worksheet 2 – Avoiding bias
Lesson 4: Decision trees
Discover how and why decision trees are used in machine learning. Learning objectives: - Describe how decision trees are used to build a classification ML model - Describe how training data changes an ML model - Explain why ML is used to create decision trees Key vocabulary: Decision tree, feature, node, root node, decision node, leaf node, classification, explainability Lesson structure: - Classification recap - What does a model look like? - Using a decision tree - Creating a decision tree - Using ML to create a decision tree - Decision trees in medicine
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Topic: Lesson 4: Decision trees
Lesson plan – Decision trees
Slides – Decision trees
Worksheet 1 – Creating a decision tree
Worksheet 2 – Creating a decision tree
Worksheet 3 – Training a decision tree
Lesson 5: How to solve problems with machine learning models
Train and test a machine learning model to solve a real-world problem. Learning objectives: - Describe the stages of the AI project lifecycle - Use a machine learning tool to import data and train a model - Test and examine the accuracy of an ML model Key vocabulary: AI project lifecycle, data cleaning, machine learning model, class, label, training, testing, accuracy, confidence score, confidence threshold Lesson structure: - Order the stages of the AI project lifecycle - User-focused approach - Stage 1: Defining the problem - Stage 2: Preparing the data - Stage 3: Training the model - Stage 4: Testing the model - Reporting on the accuracy of a model
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Topic: Lesson 5: How to solve problems with machine learning models
Lesson plan – Solving problems with ML models
Slides – Solving problems with ML models
Worksheet 1 – AI project lifecycle
Project 1 – Project documentation (Ocean data)
Project 1 – Project introduction (Ocean data)
Project 1 – Video introduction (Ocean data)
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Project 2 – Project documentation (Fake news)
Project 2 – Project introduction (Fake news)
Project 2 – Video introduction (Fake news)
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Project 3 – Project introduction (Waste classification)
Project 3 – Project documentation (Waste classification)
Lesson 6: Model cards and careers
Evaluate a project by creating a model card and learn about AI-related careers. Learning objectives: - Evaluate an ML model - Produce a model card to explain an ML model - Recognise the range of opportunities that exist in AI-related careers Key vocabulary: AI project lifecycle, machine learning model, model cards, class, label, training, testing, accuracy, confidence score, confidence threshold Lesson structure: - Predicting future crime - Evaluation and explainability - Using a model card - Create your own model card - Careers in AI and machine learning - The use of AI applications in other fields
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Topic: Lesson 6: Model cards and careers
Lesson plan – Model cards and careers
Slides – Model cards and careers
Worksheet 1 – Using a model card
Worksheet 2 – My model card
Worksheet 3 – AI careers
Summative assessment
Foundations of AI
About Lesson

Explore how AI is used in the world today and consider some of the benefits and drawbacks of AI systems.

Learning objectives:
– Describe the difference between ‘data-driven’ and ‘rule-based
– Name examples of AI applications
– Outline some benefits and issues of using AI applications

Key vocabulary:
– Artificial intelligence (AI), algorithm, data, rule-based, data-driven, model, generative AI, computer vision.

Lesson structure:
– What is intelligence
– The ‘intelligent piece of paper
– What is artificial intelligence (AI)?
– AI applications — generative AI
– AI applications — computer vision
– AI or not AI?

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