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.

Prerequisite: 

STA314H1/​ CSC411H1 (beginning Fall 2019), STA302H1, CSC108H1/​ CSC120H1/​ CSC121H1/​ CSC148H1, MAT235Y1/​ MAT237Y1/​ MAT257Y1, MAT223H1/​ MAT240H1

Exclusion: 
Recommended Preparation: 
Distribution Requirements: 
Science
Breadth Requirements: 
The Physical and Mathematical Universes (5)