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; CSC108H1/​ CSC120H1/​ CSC121H1/​ CSC148H1/​CSCA08H3/CSCA48H3/CSCA20H3/CSC108H5/CSC148H5; MAT223H1/​ MAT240H1/​MATA23H3/MAT223H5/MAT240H5; MAT235Y1/​ MAT237Y1/​ MAT257Y1/​(MATB41H3, MATB42H3)/(MAT232H5, MAT236H5)/(MAT233H5, MAT236H5)

Corequisite: 

STA302H1/​STA302H5

Exclusion: 

CSC411H1, CSC311H1, STA314H5, STA315H5, CSCC11H3, CSC411H5

Breadth Requirements: 
The Physical and Mathematical Universes (5)