Detailed Syllabus and Lectures

Lecture 2: Machine Learning Overview (slides)

types of machine learning problems, linear models, loss functions, linear regression, gradient descent, overfitting and generalization, regularization, cross-validation, bias-variance tradeoff, maximum likelihood estimation

Please study the following material in preparation for the class:

Required Reading:

• Chapter 5 of the Deep Learning text book.

Suggested Video Material:

• Machine Learning, Doina Precup (Deep Learning Summer School, Montreal 2016)

Additional Resources:

• A few useful things to know about machine learning, P. Domingos. Communications of the ACM, 55 (10), 78-87, 2012.
• The uneasy relationship between deep learning and (classical) statistics, Boaz Barak, June 2022.

Lecture 1: Introduction to Deep Learning (slides)

course information, what is deep learning, a brief history of deep learning, compositionality, end-to-end learning, distributed representations

Please study the following material in preparation for the class:

Required Reading:

• Chapter 1 of the Deep Learning text book.
• [Blog post] AI Winter. How Canadians contributed to end it?, Pavan Mirla.
• The Bandwagon, Claude E. Shannon. IRE Transactions on Information Theory, Vol. 2, Issue 3, 1956
• Chapter 1: The Philosophy and the Approach of David Marr's Vision, 1982.

Additional Resources:

• The unreasonable effectiveness of deep learning in artificial intelligence, Terrence J. Sejnowski, PNAS, 2020.
• Deep Learning, Yann LeCun, Yoshio Bengio, Geoffrey Hinton. Nature, Vol. 521, 2015.
• Deep Learning in Neural Networks: An Overview, Juergen Schmidhuber. Neural Networks, Vol. 61, pp. 85–117, 2015.
• On the Origin of Deep Learning, Haohan Wang and Bhiksha Raj, arXiv preprint arXiv:1702.07800v4, 2017