Machine Learning by Probabilistic Models

Course content

Probability, graphs, Bayesian networks, Markov networks, temporal models, state observation models, Gaussian networks, exact inference, map inference and approximate inference (sampling) in these models, probability distributions, graph parameter learning with complete and incomplete data, graph structure learning by complete and incomplete data.