Machine learning
- 1st ed.
- Chennai McGraw Hill Education 2013
- xvii, 414p.
Table of Contents
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 --
This textbook provides a single source introduction to the primary approaches to machine learning. It is intended for advanced undergraduate and graduate students, as well as for developers and researchers in the field. No prior background in artificial intelligence or statistics is assumed.