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
STA302H1/ STA302H5/ STAC67H3; CSC108H1/ CSC110Y1/ CSC120H1/ CSC148H1/ CSCA08H3/ CSCA48H3/ CSCA20H3/ CSC108H5/ CSC148H5; MAT223H1/ MAT224H1/ MAT240H1/ MATA22H3/ MATA23H3/ MAT223H5/ MAT240H5/ MATB24H3/ MAT224H5; MAT235Y1/ MAT237Y1/ MAT257Y1/ ( MATB41H3, MATB42H3)/ ( MAT232H5, MAT236H5)/ ( MAT233H5, MAT236H5)
Breadth Requirements
The Physical and Mathematical Universes (5)
Mode of Delivery
In Class