STA314H1: Statistical Methods for Machine Learning I

Hours

36L/12T

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.

Prerequisite
STA238H1/STA248H1/STA255H1/STA261H1/STAB57H3/STA260H5/STA258H5/ECO227Y1; CSC108H1/CSC110Y1/CSC120H1/CSC148H1/CSCA08H3/CSCA48H3/CSCA20H3/CSC108H5/CSC148H5; MAT223H1/MAT240H1/MATA22H3/MATA23H3/MAT223H5/MAT240H5; MAT235Y1/MAT237Y1/MAT257Y1/(MATB41H3, MATB42H3)/(MAT232H5, MAT236H5)/(MAT233H5, MAT236H5)
Corequisite
STA302H1/STA302H5/STAC67H3
Exclusion
CSC411H1, CSC311H1, STA314H5, STA315H5, CSCC11H3, CSC411H5
Breadth Requirements
The Physical and Mathematical Universes (5)