Description should include the concepts that

• in supervised learning, a model is trained on labeled data to learn the relationship between input and known outputs
• in unsupervised learning, the patterns and groupings in data are learned without preassigned labels
• in reinforcement learning, the model is trained by repeated interactions with an environment using rewards and penalties to shape behavior over time (e.g., playing chess against a computer).

Teacher Resources:

• Supervised vs. Unsupervised Learning: Understanding Their Applications, Pros, and Cons, Medium
• What’s the Difference Between Supervised and Unsupervised Learning?, Amazon Web Services (AWS)
• What is Reinforcement Learning?, AWS