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:
- the book
- additional chapter Estimating Probabilities: MLE and MAP
- additional chapter Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression
- additional chapter Key Ideas in Machine Learning
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.
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
