CSC311H1: Introduction to Machine Learning

Hours

24L/12T

Previous Course Number

CSC411H1

An introduction to methods for automated learning of relationships on the basis of empirical data. Classification and regression using nearest neighbour methods, decision trees, linear models, and neural networks. Clustering algorithms. Problems of overfitting and of assessing accuracy. Basics of reinforcement learning.

Prerequisite
CSC207H1/ APS105H1/ APS106H1/ ESC180H1/ CSC180H1; MAT235Y1/​ MAT237Y1/​ MAT257Y1/​ (minimum of 77% in MAT135H1 and MAT136H1)/ (minimum of 73% in MAT137Y1)/ (minimum of 67% in MAT157Y1)/ MAT291H1/ MAT294H1/ (minimum of 77% in MAT186H1, MAT187H1)/ (minimum of 73% in MAT194H1, MAT195H1)/ (minimum of 73% in ESC194H1, ESC195H1); MAT223H1/ MAT240H1/ MAT185H1/ MAT188H1; STA237H1/ STA247H1/ STA255H1/ STA257H1/ STA286H1/ CHE223H1/ CME263H1/ MIE231H1/ MIE236H1/ MSE238H1/ ECE286H1
Recommended Preparation
MAT235Y1/ MAT237Y1/ MAT257Y1
Exclusion
CSC411H1, STA314H1, ECE421H1, CSC311H5, CSC411H5, CSCC11H3. NOTE: Students not enrolled in the Computer Science Major or Specialist program at A&S, UTM, or UTSC, or the Data Science Specialist at A&S, are limited to a maximum of 1.5 credits in 300-/400-level CSC/ECE courses.
Distribution Requirements
Science
Breadth Requirements
The Physical and Mathematical Universes (5)
Mode of Delivery
In Class