Vector Institute - Fall 2022:

Introduction to ML for Black & Indigenous Students

This course introduces fundamental machine learning algorithms such as linear and logistic regression, random forests, decision trees, neural networks, support vector machines, boosting etc. It will also offer a broad view of model-building and optimization techniques that are based on probabilistic building blocks which will serve as a foundation for more advanced machine learning courses.

Please visit this webpage to learn more about the ML classes and internships for Black & Indigenous students at the Vector Institute. If you have questions or want to learn more about opportunities at Vector, reach out to internships@vectorinstitute.ai. For submitting assignments/project and the course related discussions, please visit your D2L account.


Announcements:


Instructors:

Prof Murat A. Erdogdu
Email introMLprof@vectorinstitute.ai
Office hours W 17-18 online

Teaching Assistants:

Ade Adeoye, Aditi Maheshwari, Mohammed Adnan, Denny Wu


Time & Location:

  Location Time Zoom link
Lecture online W 15-17 shared via email
Tutorial/Seminar online Th 15-17 shared via email

Suggested Reading

No required textbooks. Suggested reading will be posted after each lecture (See lectures below).


Lectures and timeline

Week Lecture Tutorial Weekly talk by Suggested reading Assignment
1 Introduction to ML
Nearest Neighbours
slides
colab
- ESL 1, 2.1-2.3, 2.5 A1-colab
2 Decision Trees, Ensembles
Random Forests
slides Chike Odenigbo
Manveer Singh
ESL 9.2,3 A2-colab
3 Linear Regression
Optimization I
colab Brian Ritchie ESL 3.1-3.2 A3-colab
4 Linear Classification
Optimization II
slides
colab
Kwesi Apponsah ESL 4.1,2,4 A4-colab
5 Neural Networks
Backpropagation
slides
colab
Kia Muktar NN-notes A5-colab
6 PCA, Matrix Completion
Recommender Systems
colab Lester Mackey ESL 14.5 A6-colab
7 Clustering Algorithms - Estelle Inack ESL 14.3 -
8 Fairness in ML this paper
this paper
- this paper -
- Capstone projects - - - -

Computing Resources

For the homework assignments, we will use Python, and libraries such as NumPy, SciPy, and scikit-learn. You have two options: