Introductory course to probabilistic graphical models
in machine learning. This course will cover
probabilistic modeling and algorithmic approaches to
probabilistic inference and parameter learning.
Topics will include Bayesian modeling and probability,
message passing inference algorithms, belief
propagation, variational inference, Markov chain Monte
Carlo sampling, expectation maximization, dynamical
systems, and Bayesian nonparametrics.