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Machine Learning: A Probabilistic Perspective

Introduction; Probability; Generative models for discrete data; Gaussian models; Bayesian statistics; Frequentist statistics; Linear regression; Logistic regression; Generalized linear models and the exponential family; Directed graphical models (Bayes nets); Mixture models and the EM algoritlim; Latent linear models; Sparse linear models; Kernels; Gaussian process; Adaptive basis function models; Markov and hidden Markov models; State space models; Undirected graphical models; Exact inference for graphical models; Variational inference; More variational inference; Monte Carlo inference; Markov chain Monte Carlo inference; Clustering; Graphical model structure learning; Latent variable models for discrete data; Deep learning.

Từ khóa: Machine Learning: A Probabilistic Perspective, Kevin P.Murphy

27 p ovanketv 30/12/2019 300 1

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