CSC321 Neural Networks and Machine Learning (UTM)

Course Description

Machine learning is a powerful set of techniques that allow computers to learn from data rather than having a human expert program a behaviour by hand. Neural networks are a class of machine learning algorithm originally inspired by the brain, but which have recently seen a lot of success at practical applications. They're at the heart of production systems at companies like Google and Facebook for face recognition, speech-to-text, and language understanding.

This course gives an overview of both the foundational ideas and the recent advances in neural net algorithms. Roughly the first 2/3 of the course focuses on supervised learning -- training the network to produce a specified behaviour when one has lots of labelled examples of that behaviour. The last 1/3 focuses on unsupervised learning.

See the course information sheet for more information.

Course Staff and Contact

Contact the course coordinator (Lisa) for all logistics related inquiries (e.g. medical notes, remark requests). There are other people in the University of Toronto community with the same name as Lisa. Please make sure that you are emailing the correct one.

Please use the Piazza message board for questions reqlated to course content.

All announcements will be made on Piazza and Quercus

Textbook

We will be using written notes by Prof. Roger Grosse, to be posted on the course website.

Tentative Schedule

The course schedule is tentative and subject to change.