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: 
Exclusion: 
Recommended Preparation: 
Breadth Requirements: 
The Physical and Mathematical Universes (5)