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Experience Artificial Intelligence (AI)
Lesson 1: What is AI?
Explore how AI is used in the world today and consider some of the benefits and drawbacks of AI systems.
Describe the difference between ‘data-driven’ and ‘rule-based’ approaches to application development
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 2: How computers learn from data.
Learn about the role of data-driven models in AI systems and machine learning
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
L2: How computers learn from data—slides
Video 1: What is Machine learning?
Video 2: Types of Machine learning.
[Assign form quiz on google classroom]
Key vocabulary
Machine learning (ML), training data, supervised learning, unsupervised learning, reinforcement learning, classification, class, label
Lesson 3: Bias in, Bias out
An opportunity to create your own learning model.
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
Worksheet 1: Training a model.
Worksheet 3: Avoiding bias.
Key vocabulary
Artificial intelligence (AI), machine learning (ML), supervised learning, classification, training data, test data, accuracy, bias, data bias, societal bias
Lesson 4: Decision trees
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
Creating a decision tree: Worksheet1
Worksheet: Training a decision tree.
Key vocabulary
Key vocabulary
Decision tree, feature, node, root node, decision node, leaf node, classification, explainability
Lesson 5: How to solve problems
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 a machine learning model
Starter resource: AI lifecycle
Ocean Data
Fake News
Waste Classification
Key vocabulary
AI project lifecycle, data cleaning, machine learning model, class, label, training, testing, accuracy, confidence score, confidence threshold
Lesson 6: Model Cards
Evaluate a machine learning model
Produce a model card to explain an ML model
Recognise the range of opportunities that exist in AI-related careers
Extension Units
Large Language Models
Ecosystems and AI
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