STA414 Statistical Methods for Machine Learning II

Instructor: Piotr Zwiernik, Murat A. Erdogdu

Course Overview:

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, Neural networks 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.

Course Outline

This course is mainly focus on: PGM(Bayes Net, MRF), Sampling(Importance/rejection, SMC, MCMC), HMM, Variational Inference (KL divergence, ELBO), Guassian Models(GMM, PPCA, Bayesian Linear Regression)