Description:

This course introduces how to develop deep generative models (DGMs) by integrating probabilistic graphical models and deep learning to generate realistic data including images, texts, graphs, etc. Course contents include 1) basics of probabilistic graphical models, including Bayesian network and Markov random field; 2) posterior  inference methods, including message passing, variational inference, and Markov chain Monte Carlo sampling; 3) parameter learning and structure learning methods, including maximum likelihood estimation, expectation–maximization algorithm, and graphical LASSO; 4) deep generative models (DGMs), including variational auto-encoder, generative adversarial networks, normalizing flows, and  evaluation of DGMs; 5) applications of DGMs in  image generation, text generation, and graph generation.

Prerequisite：

Machine Learning, Probability and Statistics

Reference：

https://pengtaoxie.github.io/dgm.html