Machine Learning (IE-Paperback)

Description 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.

Features No other book covers the concepts and techniques from the various fields in a unified fashion. Covers very recent subjects such as genetic algorithms, reinforcement learning, and inductive logic programming. Writing style is clear, explanatory and precise.

Table of 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. Genetic Algorithms Chapter 10. Learning Sets of Rules Chapter 11. Analytical Learning Chapter 12. Combining Inductive and Analytical Learning Chapter 13. Reinforcement Learning.