Here is a tentative schedule, which will likely change as the course goes on.
Suggested readings are just that: resources we recommend to help you understand the course material. They are not required, i.e. you are only responsible for the material covered in lecture.
ESL = The Elements of Statistical Learning,
by Hastie, Tibshirani, and Friedman.
MacKay = Information Theory, Inference, and Learning
Algorithms, by David MacKay.
Barber = Bayesian Reasoning and Machine
Learning, by David Barber.
Bishop = Pattern Recognition
and Machine Learning, by Chris Bishop.
Sutton and Barto = Reinforcement Learning: An
Introduction, by Sutton and Barto.
Goodfellow = Deep Learning, by Goodfellow, Bengio, and
Courville.
Week  Topic(s) and Dates  Slides & Suggested Readings  Important Dates 
Week 1  Introduction Nearest Neighbours, 9/10 
[Slides]
[Video]
ESL: Chapters 1, 2.12.3, and 2.5 

Week 2  Decision Trees Ensembles 9/17 
[Slides]
[Video]
ESL: 9.2, 2.9, 8.7, 15 
9/13: Hw 1 out. 
Week 3  Linear Regression Linear Classifiers, 9/26 
[Slides]
[Video]
Bishop: 3.1, 4.1, 4.3 

Week 4  Softmax Regression SVMs Boosting, 10/1 
[Slides]
[Video]
Bishop: 7.1, 14.3 
10/1: Hw1 due. 10/2: Hw2 out. 
Week 5  PCA KMeans Maximum Likelihood, 10/8 
[Slides] [Video] Bishop: 12.1, 9.1  
Week 6  Probabilistic Models, 10/15 
[Slides]
[Video]
Bishop: 2.12.3, 4.2 
10/15: Hw2 due. 10/16: Hw3 out. 
Week 7  ExpectationMaximization, 10/22 
[Slides]
[Video]
Bishop: 9.29.4 
10/22: midterm out. 10/25: midterm marks release. 
Week 8  Neural Networks, 10/29 
[Slides]
Bishop: 5.15.3 

Week 9  Convolutional Networks, 11/5 
[Slides]
Course Notes: conv
nets, image
classification 
11/5: Hw3 due. 11/6: Hw4 out. 
Week 10  Reinforcement Learning, 11/12 
[Slides]
Sutton and Barto: 3, 4.1, 4.4, 6.16.5 

Week 11  Differential Privacy, 11/19  [Slides] Dwork and Roth, 2014. The Algorithmic Foundations of Differential Privacy. Chapters 2, 3.13.5.  11/20: Hw4 due. Final project out 
Week 12  Algorithmic Fairness, 11/26 
[Slides]
Barocas, Hardt, and Narayanan. Fairness and Machine Learning. Chapters 1 and 2. Zemel et al., 2013. Learning fair representations. Louizos et al., 2015. The variational fair autoencoder. Hardt et al., 2016. Equality of opportunity in supervised learning. 

Week 13  Work on final project  
Week 14  Final project presentation, 12/10 
Most homeworks will be due on Thursdays at 11:59pm. You will submit through MarkUs; directions are given in the assignment handouts.
just like old timesOut  Due  Materials  TA Office Hours  
Homework 1  9/13  10/1 
[Handout] 
Monday Sep 28th  1011 am (EST), Monday Sep 28th  910 pm (EST) 
Homework 2  10/2  10/15 
[Handout] [q1.py] [q2.py] 
Tuesday Oct 13th  1010:30 am (EST), Tuesday Oct 13th  99:30 pm (EST) 
Homework 3  10/16  11/5 
[Handout] [code and data] 
TBD 
Homework 4  11/6  11/20  TBD  TBD 
The midterm exam will be a takehome one. The exam will be distributed online, synchronously, and you have 24 hours to complete it offline. [Exam instructions] [Midterm.pdf]
The final project will replace what used to be a final exam and students are allowed to work in a team of two. There will be a final project presentation for evaluation during lecture time on week 14 Dec 10th. More details TBD
Website and course still under development, any feedback is very appreciated, please reach out to: Sheldon, email: huang at cs dot toronto dot edu