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.

When: |
Mondays 12:45-14:45 and Thursdays 16:45-17:45 |

Where: |
Online (link available in Moodle) |

Instructor: |
Sarath ChandarOffice hours: Mondays 14:45-15:45. |

Teaching Assistants: |
Abdelrahman ZayedOffice hours: Wednesdays 13:00-14:00. Charan ReddyOffice hours: Thursdays 15:00-16:00. Lawrence AbdulnourOffice hours: Fridays 13:00-14:00. Rémi Piché-TailleferOffice hours: Tuesdays 16:00-17:00. |

Schedule: |
Available Here. |

Lecture Notes: |
Available here. |

Assignments and Tutorials: |
Available here. |

- [HTF] Trevor Hastie, Robert Tibshirani and Jerome Friedman.
*The Elements of Statistical Learning: Data Mining, Inference, and Prediction.*Available free online. - [Bishop] Christopher Bishop.
*Pattern Recognition and Machine Learning.* - [Mitchell] Tom Mitchell.
*Machine Learning.* - [TSKK] Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar.
*Introduction to Data Mining.* - [Rojas] Raul Rojas.
*Neural Networks.* - [GBC] Ian Goodfellow, Yoshua Bengio and Aaron Courville.
*Deep Learning.*Available free online

- Prof. Gilbert Strang's video lectures on linear algebra.
- Prof. John Tsitsiklis's video lectures on Applied Probability.
- Prof. Krishna Jagannathan's video lectures on Probability Theory.
- Prof. Deepak Khemani's video lectures on Artificial Intelligence.

If you do not want your video to be visible while asking questions, you can turn off your video. If you do not want your audio to be part of the recording, you can ask your questions in chat or during office hours.

- 3 Theory/Programming assignments (individual) - 45%
- Kaggle competition (team of 3) - 20%
- One endterm examination - 35%

- You will be penalized 5% if your submission is within 24 hours (1 day) from the deadline.
- You will be penalized 10% if you submission is after 24 hours from the deadline and within 48 hours (2 days) from the deadline.
- You will be penalized 20% if you submission is after 48 hours from the deadline and within 72 hours (3 days) from the deadline.
- You cannot submit your assignments/reports after 72 hours from the deadline.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.