CSC311H1: Introduction to Machine Learning



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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.

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); MAT221H1/​ MAT223H1/ MAT240H1/ MAT185H1/ MAT188H1; STA237H1/ STA247H1/ STA255H1/ STA257H1/ STA286H1/ CHE223H1/ CME263H1/ MIE231H1/ MIE236H1/ MSE238H1/ ECE286H1
CSC411H1, STA314H1, ECE421H1. NOTE: Students not enrolled in the Computer Science Major or Specialist program at FAS, UTM, or UTSC, or the Data Science Specialist at FAS, are limited to a maximum of three 300-/400-level CSC/ECE half-courses.
Distribution Requirements
Breadth Requirements
The Physical and Mathematical Universes (5)