xx, 738 p. : 25 cm Textbook for graduates Includes bibliographical references (p. 711-728) and index 1. Introduction -- 2. Probability distributions -- 3. Linear models for regression -- 4. Linear models for classification -- 5. Neural networks -- 6. Kernel methods -- 7. Sparse kernel machines -- 8. Graphical models -- 9. Mixture models and EM -- 10. Approximate inference -- 11. Sampling methods -- 12. Continuous latent variables -- 13. Sequential data -- 14. Combining models