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.
Contents Chapter 1. IntroductionChapter 2. Concept Learning and the General-to-Specific OrderingChapter 3. Decision Tree LearningChapter 4. Artificial Neural NetworksChapter 5. Evaluating HypothesesChapter 6. Bayesian LearningChapter 7. Computational Learning TheoryChapter 8. Instance-Based LearningChapter 9. Inductive Logic ProgrammingChapter 10. Analytical LearningChapter 11. Combining Inductive and Analytical LearningChapter 12. Reinforcement Learning.
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