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|
Nearest Neighbours, 9/10
ESL: Chapters 1, 2.1-2.3, and 2.5
|Week 2||Decision Trees
|[Slides] [Video]||9/13: Hw 1 out.|
|Week 3||Linear Regression
Linear Classifiers, 9/26
|Week 4||Softmax Regression
|[Slides] [Video]||10/1: Hw1 due. 10/2: Hw2 out.|
Maximum Likelihood, 10/8
|[Slides] [Video] Bishop: 12.1, 9.1|
|Week 6||Probabilistic Models, 10/15||
Bishop: 2.1-2.3, 4.2
|10/15: Hw2 due.
10/16: Hw3 out.
|Week 7||Expectation-Maximization, 10/22||
|10/22: midterm out.
10/25: midterm marks release.
|Week 8||Neural Networks, 10/29||[Slides]|
|Week 9||Convolutional Networks, 11/5||[Slides]||11/5: Hw3 due. 11/6: Hw4 out.|
|Week 10||Reinforcement Learning, 11/12||
Sutton and Barto: 3, 4.1, 4.4, 6.1-6.5
|Week 11||Differential Privacy, 11/19||[Slides] Dwork and Roth, 2014. The Algorithmic Foundations of Differential Privacy. Chapters 2, 3.1-3.5.||11/20: Hw4 due. Final project out|
|Week 12||Algorithmic Fairness, 11/26||
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.
|Out||Due||Materials||TA Office Hours|
||Monday Sep 28th - 10-11 am (EST), Monday Sep 28th - 9-10 pm (EST)|
|Tuesday Oct 13th - 10-10:30 am (EST), Tuesday Oct 13th - 9-9:30 pm (EST)|
[code and data]
The midterm exam will be a take-home 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