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.
Prof | Murat A. Erdogdu |
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introMLprof@vectorinstitute.ai | |
Office hours | W 17-18 online |
Ade Adeoye, Aditi Maheshwari, Mohammed Adnan, Denny Wu
Location | Time | Zoom link | |
---|---|---|---|
Lecture | online | W 15-17 | shared via email |
Tutorial/Seminar | online | Th 15-17 | shared via email |
No required textbooks. Suggested reading will be posted after each lecture (See lectures below).
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 | - | - | - | - |
For the homework assignments, we will use Python, and libraries such as NumPy, SciPy, and scikit-learn. You have two options:
pip install scipy numpy autograd matplotlib jupyter sklearn