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This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning--including probability and statistics, artificial intelligence, and neural networks--unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students. 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. Inductive Logic Programming Chapter 10. Analytical Learning Chapter 11. Combining Inductive and Analytical Learning Chapter 12. Reinforcement Learning.