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/ MAT224H1/ MAT240H1/ MATA22H3/ MATA23H3/ MAT223H5/ MAT240H5/ MATB24H3/ MAT224H5
Recommended Preparation
Distribution Requirements
Science
Breadth Requirements
The Physical and Mathematical Universes (5)
Mode of Delivery
In Class