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/​ CSC311H1/​(STA314H5, STA315H5)/CSCC11H3/CSC411H5; STA302H1/​STAC67H3/STA302H5; CSC108H1/​ CSC120H1/​ CSC121H1/​ CSC148H1/​CSCA08H3/CSCA48H3/CSCA20H3/CSC108H5/CSC148H5; MAT235Y1/​ MAT237Y1/​ MAT257Y1/​(MATB41H3, MATB42H3)/(MAT232H5, MAT236H5)/(MAT233H5, MAT236H5); MAT223H1/​ MAT240H1/​MATA23H3/MAT223H5/MAT240H5

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