This course provides a rigorous introduction to the field of machine learning (ML). The aim of the course is not just to teach how to use ML algorithms but also to explain why, how, and when these algorithms work. The course introduces fundamental algorithms in supervised learning and unsupervised learning from the first principles. The course, while covering several problems in machine learning like regression, classification, representation learning, dimensionality reduction, will introduce the core theory, which unifies all the algorithms.

This course will be offered in English. However, the students in this course can submit in English or French any written work that is to be graded.

Quebec university students from outside Polytechnique Montreal can register for the course via Inter-University Transfer Authorization.

If you are a student at Poly, UdeM, HEC, McGill, or Mila, then you can request to audit this course by filling this Google Form.

General Information

Mondays 12:45 pm to 03:45 pm (starting from 30 Aug)

Building - Pavillons Pierre-Lassonde et Claudette McKay-Lassonde , Ground Floor
Nearest metro - Université-de-Montréal
This course is structured to be an in-person course. However, you can join remotely if you cannot attend in-person.

About Labs
The official slots for lab sessions for this course are either Wednesdays 3:45 pm to 6:45 pm biweekly or Fridays 8:30 am to 11:30 am biweekly depending on your group. However, we will not do regular labs during these slots. We will have few online tutorials during these timeslots. You can use the rest of the lab time to work on the practical assignments by yourself. Additionally, we will have one-hour office hours every day from Monday to Friday where students can ask TAs their doubts about the practical assignments.



Office Hours

Name. Day Time
Sarath Chandar Monday 3:45 pm to 4:45 pm (M-3406)
Valliappan CA Tuesday 11:00 am to 12:00 pm
Arjun VS Wednesday 3:00 pm to 4:00 pm
Gabriele Prato Thursday 10:00 am to 11:00 am
Abdelrahman Zayed Friday 11:30 am to 12:30 pm
  • Nadhir Hassen