【機械学習】
Machine Learning(Adaptive Computation and Machine Learning series) hardcover 1104 p., 300 color illus., 165 b&w illus. 12
Murphy, Kevin P., Bach, Francis 著
内容
目次
1. Introduction 2. Probability 3. Generative models for discrete data 4. Gaussian models 5. Bayesian statistics 6. Frequentist statistics 7. Linear regression 8. Logistic regression 9. Generalized linear models and the exponential family 10. Directed graphical models (Bayes nets) 11. Mixture models and the EM algorithm 12. Latent linear models 13. Sparse linear models 14. Kernels 15. Gaussian processes 16. Adaptive basis function models 17. Markov and hidden Markov models 18. State space models 19. Undirected graphical models (Markov random fields) 20. Exact inference for graphical models 21. Variational inference 22. More variational inference 23. Monte Carlo inference 24. Markov chain Monte Carlo (MCMC) inference 25. Clustering 26. Graphical model structure learning 27. Latent variable models for discrete data 28. Deep learning