CSC 412/2506 Probabilistic Machine Learning

Teaching Assistant

This course introduces probabilistic learning tools such as exponential families, directed graphical models, Markov random fields, exact inference techniques, message passing, sampling and mcmc, hidden Markov models, variational inference, EM algorithm, bayesian regression, probabilistic PCA, kernel methods, Gaussian processes, and variational autoencoders. It will also offer a broad view of model-building and optimization techniques that are based on probabilistic building blocks which will serve as a foundation for more advanced machine learning courses.