Chapter 15: Machine Learning Basics

Learning Objectives

By the end of this chapter, you will:

• Understand supervised, unsupervised, and reinforcement learning
• Learn about training and testing
• Understand overfitting
• Learn about common ML algorithms

Introduction

Machine learning enables robots to learn from data and improve their performance over time.

Placeholder Content

Topics to be covered:

• ML paradigms
• Training and validation
• Feature engineering
• Common algorithms (regression, classification, clustering)

Did You Know?

Interesting ML fact.

Real-World Examples

1. Recommender Systems - Product suggestions
2. Spam Detection - Email filtering
3. Handwriting Recognition - Digit classification

Key Takeaways

• To be added

Try It!

Simple ML classification activity.

Exercises

1. Question
2. Question
3. Question

Further Reading

• Chapter 16: Deep Learning for Vision
• Chapter 17: Reinforcement Learning

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Next Chapter: Chapter 16: Deep Learning for Vision