English
资源说明：机器学习综合概述，主要是关于unsupervised learning，We give a tutorial and overview of the field of unsupervised learning from the perspective of statistical
modelling. Unsupervised learning can be motivated from information theoretic and Bayesian principles.
We briefly review basic models in unsupervised learning, including factor analysis, PCA, mixtures of
Gaussians, ICA, hidden Markov models, state-space models, and many variants and extensions. We
derive the EM algorithm and give an overview of fundamental concepts in graphical models, and inference
algorithms on graphs. This is followed by a quick tour of approximate Bayesian inference, including
Markov chain Monte Carlo (MCMC), Laplace approximation, BIC, variational approximations, and
expectation propagation (EP). The aim of this chapter is to provide a high-level view of the field. Along
the way, many state-of-the-art ideas and future directions are also reviewed
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