Machine Learning, Tom Mitchell, McGraw Hill, 1997.


Machine Learning is the study of computer algorithms that improve automatically through experience.

This book provides a single source introduction to the field. It is written for advanced undergraduate and graduate students, and for developers and researchers in the field. No prior background in artificial intelligence or statistics is assumed.

Free pdf downloads:

Machine Learning course using this book plus supplemental readings, taught in 2011 (includes video lectures, online slides, homeworks, exams)

Software and data discussed in the text.

Errata for printings one and two

About the author.

Reviews of this book.

Chapter Outline:

  • 1. Introduction
  • 2. Concept Learning and the General-to-Specific Ordering
  • 3. Decision Tree Learning
  • 4. Artificial Neural Networks
  • 5. Evaluating Hypotheses
  • 6. Bayesian Learning
  • 7. Computational Learning Theory
  • 8. Instance-Based Learning
  • 9. Genetic Algorithms
  • 10. Learning Sets of Rules
  • 11. Analytical Learning
  • 12. Combining Inductive and Analytical Learning
  • 13. Reinforcement Learning
414 pages. ISBN 0070428077