MLPR 2019  |  Notes  |  Lectures  |  Forum  |  Tutorials  |  Assignments  |  Feedback

MLPR class notes

These notes were written from scratch for this class. We will respond to your comments and questions, and fix or expand parts if and when necessary. However, effort from you is also required. Please sign up to the forum, and ask questions.

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Each note links to a PDF version for better printing. However, if possible, please annotate the HTML versions of the notes in the forum, to keep the class's comments together. If the HTML notes don't render well for you, you could try in Chrome/Chromium. If you want quick access to the PDFs from this page, you can toggle the pdf links.

A rough indication of the schedule is given, although we won’t follow it exactly.

Background information

Week 1:

Week 2:

Week 3:

Week 4:

Week 5:

Week 6:

Week 7:

Week 8:

Week 9:

Week 10:

Bonus material (non-examinable):


Week 11: No lectures, two Ed-Intelligence events:


A coarse overview of major topics covered is below. Some principles aren't taught alone as they're useful in multiple contexts, such as gradient-based optimization, different regularization methods, ethics, and practical choices such as feature engineering or numerical implementation.

You are encouraged to write your own outlines and summaries of the course. Aim to make connections between topics, and imagine trying to explain to someone else what the main concepts of the course are.


MLPR 2019  |  Notes  |  Lectures  |  Forum  |  Tutorials  |  Assignments  |  FAQ  |  Feedback