Topic 6D - Types of Learning

Learning Objectives

By the end of this topic, you should be able to:

• Define and distinguish between supervised learning, unsupervised learning, and reinforcement learning, and give a real-world example of each.

Learning Activities

To help you meet the learning objectives, we have prepared three readings. Please complete them in order.

Readings

• Reading 1 - What Does It Mean to Learn? — what distinguishes a system that learns from one that merely follows rules, and why that distinction matters for understanding modern AI
• Reading 2 - Supervised and Unsupervised Learning — the two label-based learning categories, their defining differences, and concrete examples of each
• Reading 3 - Reinforcement Learning and the Big Picture — learning through trial, reward, and penalty; how the three types compare; and the conceptual map you will use to navigate Week 7

These readings intentionally build on each other, so please complete them in order.

Checking for Understanding

Review the Learning Objectives at the top of this page. The questions below will help you check your understanding before the Week 6 small group discussion and in preparation for the AI Competency Demo.

What Learning Is

1. A thermostat turns the heat on when the temperature drops below a set point. A spam filter adjusts its classification rules based on which messages users mark as spam. Which system is learning, and which is just following rules? What is the key difference?
2. In your own words, explain what a "model" is in the context of machine learning. What does a model do that the raw training data cannot do on its own?

Supervised and Unsupervised Learning

3. For each of the following, identify whether it is an example of supervised or unsupervised learning, and explain your reasoning:
   • A system trained on thousands of labeled X-ray images (each labeled "pneumonia" or "no pneumonia") to detect pneumonia in new X-rays.
   • A system that analyzes five years of student performance data and discovers three distinct student profiles that no administrator had previously identified.
   • A handwriting recognition system trained on labeled examples of handwritten digits.
   • A music streaming service that groups listeners into clusters based on listening patterns, without any predefined genre categories.
4. Supervised learning requires labeled training data. Where do those labels come from? Describe one scenario where labeling is easy and one where it is expensive or difficult. What are the implications for which problems supervised learning can be applied to?

Reinforcement Learning

5. A game-playing agent starts knowing nothing about the rules of chess. After playing millions of games against itself, it becomes stronger than any human player. What type of learning is this? What signal does the agent use to improve?
6. Explain why reinforcement learning is a better fit than supervised learning for teaching an agent to play a video game. What would supervised learning require that is impractical in this context?

Putting It Together

7. A school district wants to build an AI system that predicts which students are likely to struggle on a state standardized test, using three years of historical student data where test outcomes are known.
   • Which learning type is most appropriate here? Why?
   • What would the training data need to include?
   • What ethical questions should the district ask before deploying such a system? (Connect to the Week 5 SEC scenarios if helpful.)
8. The three learning types will each appear in Week 7 as specific techniques. Without looking ahead, predict: which type do you think will be hardest to implement? Which do you think produces the most transparent, explainable results? Be ready to revisit your answers after Week 7.

It is completely fine to revisit the readings as you work through these questions.

Extend Your Learning