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COMP 551: Applied Machine Learning - Winter 2022

Contact: comp551mcgill@gmail.com
please make sure to use this email to receive a timely response

Class Times & Location
Teaching Team

FAQ

  • Do I have the prerequisites to take the course?
  • This course requires strong Python programming skills and basic knowledge of probabilities, [multivariate] calculus and linear algebra. Please check this quiz to test if your background is strong enough for taking the course. It can also be used to diagnose where your background might be lacking and be used to self-study before taking the course. Most concepts covered in these questions will be used throughout the course in the slides.
  • Do I need instructor's permission to take this course?
  • It is up to your judgment to see if your background is sufficient. If in doubt, please take the prereq quiz [it is perfectly okay if you need to look up things but you need to follow and be comfortable with the questions after some time investment]. Also check the slides from the last year and see if you can follow them. There is no need to reach out to me for a permission; please reach out directly to the admins to help you register and cc me, in case you can not register directly and feel confident with your background.
  • How similar is it to the last years?
  • Very similar, please check the last year's websites to get a glimpse of the slides, expectations, etc. We will have an updated version and not exactly the same materials but very similar overall.
  • Will there be lecture recordings?
  • Yes, and class participation is not mandatory.
  • What do I learn in this course?
  • You will learn how the most common machine learning algorithms are designed, how they are implemented, and how to apply them in practice. This course has a heavy theory component, since it is important to understand the inner-workings of the algorithms in order to effectively utilize them in practice. Please check below for more information and note that everything below is tentative.

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    Overview

    Textbooks

    Schedule

    Tutorials

    Outline

    Evaluation

    Academic Integrity

    Online Resources