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/CSC110Y1/CSC120H1/CSC148H1/CSCA08H3/CSCA48H3/CSCA20H3/CSC108H5/CSC148H5; MAT235Y1/MAT237Y1/MAT257Y1/(MATB41H3, MATB42H3)/(MAT232H5, MAT236H5)/(MAT233H5, MAT236H5); MAT223H1/MAT240H1/MATA22H3/MATA23H3/MAT223H5/MAT240H5
Exclusion
CSC412H1; STAD68H3
Distribution Requirements
Science
Breadth Requirements
The Physical and Mathematical Universes (5)