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, 2.1-2.3, 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 K-Means Maximum Likelihood, 10/8 |
[Slides] [Video] Bishop: 12.1, 9.1 | |
| Week 6 | Probabilistic Models, 10/15 |
[Slides]
[Video]
Bishop: 2.1-2.3, 4.2 |
10/15: Hw2 due. 10/16: Hw3 out. |
| Week 7 | Expectation-Maximization, 10/22 |
[Slides]
[Video]
Bishop: 9.2-9.4 |
10/22: midterm out. 10/25: midterm marks release. |
| Week 8 | Neural Networks, 10/29 |
[Slides]
[Video]
Bishop: 5.1-5.3 |
|
| Week 9 | Convolutional Networks, 11/5 |
[Slides]
[Video]
Course Notes: conv
nets, image
classification |
11/5: Hw3 due. 11/6: Hw4 out. |
| Week 10 | Reinforcement Learning, 11/12 |
[Slides]
[Video]
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. 11/21: 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 | ||
| Week 15 | Final project report, 12/15 |
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 | |
| Homework 1 | 9/13 | 10/1 |
[Handout] |
Monday Sep 28th - 10-11 am (EST), Monday Sep 28th - 9-10 pm (EST) |
| Homework 2 | 10/2 | 10/15 |
[Handout] [q1.py] [q2.py] |
Tuesday Oct 13th - 10-10:30 am (EST), Tuesday Oct 13th - 9-9:30 pm (EST) |
| Homework 3 | 10/16 |
|
[Handout] [code and data] |
Nov. 2nd Monday 2nd, 10-10:30 am (EST), Nov. 2nd Monday 2nd, 9-9:30 pm (EST) |
| Homework 4 | 11/6 | 11/20 |
[Handout] |
Nov. 17 th Monday 2nd, 10-10:30 am (EST), Nov. 17 th Monday 2nd, 9-9:30 pm (EST) |
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 and a final project report due on Dec 15th.
[Project Instructions]
[Presentation Rubric]
[Report Rubric]
Website template. Any feedback is very appreciated, please reach out to: Sheldon, email: huang at cs dot toronto dot edu
