Preface Notations 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. Multiplayer 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; a. Probability
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