Statistical Methods for Machine Learning II
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.
Prerequisites & course information
Prerequisite: STA314H1 / CSC311H1 / CSC311H5 / ( STA314H5 , STA315H5 )/ CSCC11H3 ; STA302H1 / STAC67H3 / STA302H5 ; CSC108H1 / CSC110Y1 / CSC148H1 / CSCA08H3 / CSCA48H3 / CSCA20H3 / CSC108H5 / CSC148H5 ; ( MAT235H1 , MAT236H1 )/ MAT235Y1 / MAT237Y1 / MAT257Y1 / ( MATB41H3 , MATB42H3 )/ ( MAT232H5 , MAT236H5 )/ ( MAT233H5 , MAT236H5 ); MAT223H1 / MAT224H1 / MAT240H1 / MATA22H3 / MATA23H3 / MAT223H5 / MAT240H5 / MATB24H3 / MAT224H5; Prerequisite:; Exclusion: CSC412H1 , STAD68H3; Exclusion:
Sources
- Course catalogue ↗2026-2027