This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

Contents:
Chapter 1. Introduction Chapter
2. Concept Learning and the General-to-Specific Ordering Chapter
3. Decision Tree Learning Chapter
4. Artificial Neural Networks Chapter
5. Evaluating Hypotheses Chapter
6. Bayesian Learning Chapter
7. Computational Learning Theory Chapter
8. Instance-Based Learning Chapter
9. Inductive Logic Programming Chapter
10. Analytical Learning Chapter
11. Combining Inductive and Analytical Learning Chapter
12. Reinforcement Learning.