Includes bibliographical references and index

1. Introduction --
2. Supervised learning --
3. Bayesian decision theory --
4. Parametric methods --
5. Multivariate methods --
6. Dimensionality reduction --
7. Clustering --
8. Nonparametric methods --
9. Decision trees --
10. Linear discrimination --
11. Multilayer perceptrons --
12. Local models --
13. Kernel machines --
14. Graphical models --
15. Hidden markov models --
16. Bayesian estimation --
17. Combining multiple learners --
18. Reinforcement learning --
19. Design and analysis of machine learning experiments.

Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of this title reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.