The course is an introduction to probabilistic methods in machine learning. Unlike in standard machine learning, the models in probabilistic machine learning are probability distributions on the observations and the parameters. The course covers various aspects of probabilistic machine learning, including: Bayesian workflow, probabilistic graphical models (directed and undirected), Hidden Markov Models, exact inference, variational inference, MCMC, approximate Bayesian computation, Gaussian processes.
