Statistical Methods for Machine Learning I
Statistical methods for supervised and unsupervised learning from data: training error, test error and cross-validation; classification, regression, and logistic regression; principal components analysis; stochastic gradient descent; decision trees and random forests; k-means clustering and nearest neighbour methods. Computational tutorials will support the efficient application of these methods.
Prerequisites & course information
Prerequisite: STA302H1 / STA302H5 / STAC67H3 ; CSC108H1 / CSC110Y1 / CSC148H1 / CSCA08H3 / CSCA48H3 / CSCA20H3 / CSC108H5 / CSC148H5 ; MAT223H1 / MAT224H1 / MAT240H1 / MATA22H3 / MATA23H3 / MAT223H5 / MAT240H5 / MATB24H3 / MAT224H5 ; ( MAT235H1 , MAT236H1 )/ MAT235Y1 / MAT237Y1 / MAT257Y1 / ( MATB41H3 , MATB42H3 )/ ( MAT232H5 , MAT236H5 )/ ( MAT233H5 , MAT236H5 ); Prerequisite:; Exclusion: CSC311H1 , CSC311H5 , STA314H5 , STA315H5 , CSCC11H3; Exclusion:
Sources
- Course catalogue ↗2026-2027