Machine Learning is the study of computer algorithms that improve
automatically through experience. Applications range from datamining
programs that discover general rules in large data sets, to
information filtering systems that automatically learn users'
interests.
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.
Chapter Outline: (or see the detailed
table of contents (postscript))
- 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
New book chapters available for download.
Reviews of this book.
Ordering information.
Lecture slides for instructors, in both postscript and latex source
Software and data discussed in the text.
Errata for printings one and two ( postscript )( pdf )
About the author.