This course is a graduate level introduction to probabilistic models and inference algorithms, which constitute a common foundation for many methodologies in machine learning and related fields (e.g. computer vision, natural language processing, and data mining).

The course begins with a detailed exposition of probabilistic graphical models, then proceeds with various inference methods, including variational inference, belief propagation, and Markov Chain Monte Carlo (MCMC). In the second part of the course, we then discuss the connections between probabilistic models and risk minimization, as well as how optimization-based methods can be used in
large-scale model estimation. Finally, the course will briefly discuss nonparametric models, e.g. Gaussian processes, and their use in practical applications.

Advisory: Basic knowledge on linear algebra, probability theory, optimization are required.