This is a tentative schedule only. As the course progresses, the schedule will be adjusted.
Lecture  Date  Topic  Readings (textbooks) 

1  May 6  Introduction to Machine Learning (Lecture slides) (video)  
2  May 8  Knearest neighbours (Lecture slides (slide 20 revised on Jan 11)) (video)  [RN] Sec. 18.8.1, [HTF] Sec. 2.3.2, [D] Chapt. 3, [B] Sec. 2.5.2, [M] Sec. 1.4.2 
3  May 13  Linear regression (Lecture slides) (Notation Reference Sheet) (video)  [RN] Sec. 18.6.1, [HTF] Sec. 2.3.1, [D] Sec. 7.6, [B] Sec. 3.1, [M] Sec. 1.4.5 
4  May 15  Statistical learning (Lecture slides) (video)  [RN] Sec. 20.1, 20.2, [M] Sec. 2.2, 3.2 
May 20  No Lecture (Victoria Day)  
5  May 22  Linear regression by maximum likelihood, maximum a posteriori, Bayesian learning (Lecture slides (Slide 14 revised on June 24)) (video)  [B] Sec. 3.13.3, [M] Chap. 7 
May 24  Assignment 1 due (11:59 pm)  
6  May 27  Project ideas:


7  May 29  Mixture of Gaussians (Lecture slides) (video)  [B] Sec. 4.2, [M] Sec. 4.2 
8  Jun 3  Logistic regression, generalized linear models (Lecture slides (Slide 11 revised on June 20, Slide 18 revised on June 26)) (video)  [RN] Sec. 18.6.4, [B] Sec. 4.3, [M] Chap. 8, [HTF] Sec. 4.4 
9  Jun 5  Perceptrons, single layer neural networks (Lecture slides)(video)  [D] Chapt. 4, [HTF] Chapt. 11, [B] Sec. 4.1.7, 5.1, [M] Sec. 8.5.4, [RN] Sec. 18.7 
Jun 10  Assignment 2 due (11:59 pm)  
10  Jun 10  Multilayer neural networks, backpropagation (Lecture slides) (video)  [D] Chapt. 10, [HTF] Chapt. 11, [B] Sec. 5.2, 5.3, [M] Sec. 16.5, [RN] Sec. 18.7 
11  Jun 12  Kernel methods (Lecture slides) (video)  [D] Chapt. 11, [B] Sec. 6.1, 6.2 [M] Sec. 14.1, 14.2 [HTF] Chap. 6 
Jun 14  Project proposal due (11:59 pm)  
12  Jun 17  Gaussian Processes (Lecture slides (slide 11 revised on June 24)) (video)  [B] Sec. 6.4 [M] Chap. 15 [HTF] Sec. 8.3 
13  Jun 19  Support vector machines (Lecture slides (Slide 20 revised on June 24)) (video)  [B] Sec. 7.1 [D] Sec. 11.511.6 [HTF] Chap. 12 [M] Sec. 14.5 [RN] Sec. 18.9 [MRT] Chap. 4 
Jun 21  Midterm (8:30 pm  9:50 pm)


14  Jun 24  Support vector machines continued (Lecture slides) (video)  [B] Sec. 7.1 [D] Sec. 6.7 [HTF] Chap. 12 [M] Sec. 14.5 [RN] Sec. 18.9 [MRT] Chap. 4 
15  Jun 26  Deep neural networks (Lecture slides) (video)  [GBC] Chap. 6, 7, 8 
Jun 30  Assignment 3 due (11:59 pm)  
July 1  No Lecture (Canada Day) (The lecture is moved to Tuesday July 2, exceptionally)  
16  Jul 2  Convolutional neural networks (Lecture slides (slide 6 revised July 10)) (video)  [GBC] Chap. 9 
17  Jul 3  Hidden Markov models (Lecture slides (Slide 16 revised August 12)) (video)  [RN] Sec. 15.3 [B] Sec. 13.113.2 [M] Sec. 17.317.5 
18  Jul 8  Recurrent neural networks (Lecture slides (slide 13 revised July 13)) (video)  [GBC] Chap. 10 
19  Jul 10  Attention and transformer networks (Lecture slides (slides 5, 6, 8, 9 revised July 13, 15, 30)) (video)  [Vaswani et al., Attention is All You Need, NeurIPS, 2017] 
Jul 12  Assignment 4 due (11:59 pm)  
20  Jul 15  Autoencoders (Lecture slides) (video)  [GBC] Chap. 14 
21  Jul 17  Generative networks (variational autoencoders and generative adversarial networks) (Lecture slides) (video)  [GBC] Chap. 20 
22  Jul 22  Ensemble learning: bagging and boosting (Lecture slides) (video)  [RN] Sec 18.10, [M] Sec. 16.2.5, [B] Chap. 14, [HTF] Chap. 1516, [D] Chap. 11 
23  Jul 24  Normalizing Flows (guest lecture by Priyank Jaini) (Lecture slides) (video)  
24  Jul 29  Gradient boosting, bagging, decision forests (Lecture slides (Slide 5 revised August 13)) (video)  [RN] Sec 18.10, [M] Sec. 16.2.5, 16.4.5, [B] Chap. 14, [HTF] Chap. 10, 1516, [D] Chap. 13 
Jul 30  Assignment 5 due (11:59 pm)  
Aug 9  Project report due (11:59 pm) 