STA414H1: Statistical Methods for Machine Learning II

Hours

36L

Probabilistic foundations of supervised and unsupervised learning methods such as naive Bayes, mixture models, and logistic regression. Gradient-based fitting of composite models including neural nets. Exact inference, stochastic variational inference, and Marko chain Monte Carlo. Variational autoencoders and generative adversarial networks.

Exclusion
Distribution Requirements
Science
Breadth Requirements
The Physical and Mathematical Universes (5)
Mode of Delivery
In Class