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

1  Jan 3  Introduction to Machine Learning (Lecture slides)  
2  Jan 8  Knearest neighbours (Lecture slides (Slide 24 revised on Jan 11))  [RN] Sec. 18.8.1, [HTF] Sec. 2.3.2, [D] Chapt. 3, [B] Sec. 2.5.2, [M] Sec. 1.4.2 
3  Jan 10  Linear regression (Lecture slides) (Notation Reference Sheet)  [RN] Sec. 18.6.1, [HTF] Sec. 2.3.1, [D] Sec. 7.6, [B] Sec. 3.1, [M] Sec. 1.4.5 
4  Jan 15  Statistical learning (Lecture slides)  [RN] Sec. 20.1, 20.2, [M] Sec. 2.2, 3.2 
5  Jan 17  Linear regression by maximum likelihood, maximum a posteriori, Bayesian learning (Lecture slides (Slide 11 revised Jan 24, Slide 14 revised Feb 26))  [B] Sec. 3.13.3, [M] Chap. 7 
Jan 19  Assignment 1 due (11:59 pm)  
6  Jan 22  Mixture of Gaussians (Lecture slides (Slide 16 revised Jan 24, Slide 11 revised Feb 2))  [B] Sec. 4.2, [M] Sec. 4.2 
7  Jan 24  Logistic regression, generalized linear models (Lecture slides (Slide 11 revised Feb 12, Slide 5 revised Jan 30))  [RN] Sec. 18.6.4, [B] Sec. 4.3, [M] Chap. 8, [HTF] Sec. 4.4 
8  Jan 29  Perceptrons, single layer neural networks (Lecture slides (Slide 24 revised Feb 12))  [D] Chapt. 4, [HTF] Chapt. 11, [B] Sec. 4.1.7, 5.1, [M] Sec. 8.5.4, [RN] Sec. 18.7 
9  Jan 31  Multilayer neural networks, backpropagation (Lecture slides)  [D] Chapt. 10, [HTF] Chapt. 11, [B] Sec. 5.2, 5.3, [M] Sec. 16.5, [RN] Sec. 18.7 
Feb 2  Assignment 2 due (11:59 pm)  
10  Feb 5  Guest lecture by Joseph D'Souza (ProNavigator), Nabiha Asghar (UW and ProNavigator) (ProNavigator lecture slides), Francois Chaubard (Focal Systems) and Agastya Kalra (Focal Systems)  
11  Feb 7  Kernel methods (Lecture slides)  [D] Chapt. 11, [B] Sec. 6.1, 6.2 [M] Sec. 14.1, 14.2 [H] Chap. 9 [HTF] Chap. 6 
12  Feb 12  Gaussian Processes (Lecture slides)  [B] Sec. 6.4 [M] Chap. 15 [HTF] Sec. 8.3 
13  Feb 14  Midterm (in class)


Feb 19  Family day (no class)  
Feb 21  Reading break (no class)  
14  Feb 26  Support vector machines (Lecture slides)  [B] Sec. 7.1 [D] Sec. 11.511.6 [HTF] Chap. 12 [M] Sec. 14.5 [RN] Sec. 18.9 [MRT] Chap. 4 
Feb 26  Project proposal due (11:59 pm)  
15  Feb 28  Support vector machines continued (Lecture slides)  [B] Sec. 7.1 [D] Sec. 6.7 [HTF] Chap. 12 [M] Sec. 14.5 [RN] Sec. 18.9 [MRT] Chap. 4 
Mar 2  Assignment 3 due (11:59 pm)  
16  Mar 5  Deep neural networks (Lecture slides)  [GBC] Chap. 6, 7, 8 
17  Mar 7  Convolutional neural networks (Lecture slides)  [GBC] Chap. 9 
18  Mar 12  Hidden Markov models (Lecture slides)  [RN] Sec. 15.3 [B] Sec. 13.113.2 [M] Sec. 17.317.5 
19  Mar 14  Recurrent and recursive neural networks (Lecture slides (Slide 10 revised March 21))  [GBC] Chap. 10 
Mar 16  Assignment 4 due (11:59 pm)  
20  Mar 19  Autoencoders (Lecture slides (Slide 9 revised March 21))  [GBC] Chap. 14 
21  Mar 21  Generative networks (variational autoencoders and generative adversarial networks) (Lecture slides)  [GBC] Chap. 20 
22  Mar 26  Ensemble learning: bagging and boosting (Lecture slides)  [RN] Sec 18.10, [M] Sec. 16.2.5, [B] Chap. 14, [HTF] Chap. 1516, [D] Chap. 11 
23  Mar 28  Bagging, decision forests, distributed computing (Lecture slides)  [RN] Sec 18.10, [M] Sec. 16.2.5, [B] Chap. 14, [HTF] Chap. 1516, [D] Chap. 11, [M] Sec. 8.5 
Mar 30  Assignment 5 due (11:59 pm)  
24  Apr 2  Guest Lecture by Focal Systems: Machine Learning in the Real World  
Apr 10  Final exam 12:303pm M31006  
Apr 15  Project report due (11:59 pm) 