Machine learning : a probabilistic perspective/
Kevin P. Murphy
- xxix, 1067 páginas : ilustraciones
- Adaptive computation and machine learning series .
1. Introduction -- 2. Probability -- 3. Generative models -- 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. Gaussians processes -- 16. Adaptative 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.-