CSC321H1: Introduction to Neural Networks and Machine Learning


The first half of the course is about supervised learning for regression and classification problems and will include the perceptron learning procedure, backpropagation, and methods for ensuring good generalisation to new data. The second half of the course is about unsupervised learning methods that discover hidden causes and will include K-means, the EM algorithm, Boltzmann machines, and deep belief nets.


(MAT136H1 with a minimum mark of 77)/(MAT137Y1 with a minimum mark of 73)/(MAT157Y1 with a minimum mark of 67)/MAT235Y1/​MAT237Y1/​MAT257Y1, MAT221H1/​MAT223H1/​MAT240H1; STA247H1/​STA255H1/​STA257H1

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