Machine Learning: A Probabilistic Perspective > 인공지능

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