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| MATH60629A | Lectures | Project | Office hours

Machine Learning for Large-Scale Data Analysis and Decision Making (MATH60629A): Autumn 2025

Course Description

Welcome to MATH60629A graduate level course on introduction to machine learning at HEC Montreal (English edition). In this course, we will study machine learning models, a type of statistical analysis that focuses on prediction, for analyzing very large datasets ("big data"). The plan is to survey different machine learning techniques (supervised, unsupervised, reinforcement learning) as well as some applications (e.g., recommender systems).

Course Format

This course will be given as a flipped classroom. It is an instructional strategy where students learn the material before they come to the class. The material will be a mix of readings and video capsules. Class time is reserved for more active activities such as problem solving, demonstrations, and questions-answering. In addition, class time will contain a short summary of the week's material.

Time & Room

  • Tuesdays 3:30 pm - 6:30 pm
  • Room: C-Ste-Cath, Groupe Cholette

Prerequisites

Mathematical maturity and basic knowledge of statistics, and probability will be assumed. For the programming assignments and the project, Python programming will be assumed.

If you do not know Python here are few ways to learn the basics below.

  • Data Camp: Complete Chapters 1, 2, 3, 4 of Introduction to Python (sign in using this link with your @hec.ca email address to access Chapters 2-4). This option is highly recommended.
  • HEC CAM offers introductory python courses in September (currently only in French). Register at CAM registration.
  • Fall 2018 tutorial. This will give you an idea of the level that is expected for this course.

Further a machine-learning tutorial using python will be provided on week #4.

Grading

Your final score for the course will be computed using the following weights:

  1. Homework (20%)
  2. Project (30%)
  3. Project presentation (10%)
  4. Final Exam (30%)
    • Date: December 16th, Time: 1:30 pm - 4:30 pm,
    • Documentation allowed: cheat sheet (standard size 8.5 x 11, double sided), calculator.
    • Material covered: Everything covered in class + required lectures.
    • Past exams: Fall 2018, Fall 2020 (Solutions)
  5. Capsule summaries (10%)
    • Provide a short summary (10 to 15 lines of text in the form) of 10 capsules throughout the semester.
    • The summary of a capsule must be provided before its class (e.g., a summary of capsule on "Learning Problems" must be submitted by 09/02).
    • Post your summaries with this form.

ATTENTION regarding fraud and plagiarism: The HEC Montreal has a strict policy in case of fraud or plagiarism. If an infraction is found, the professor is required to report to the director of the department. An administrative procedure is then automatically triggered with the following consequences: the offense is noted in your file, and a sanction is decided (which can be serious and go to dismissal in case of recidivism). It is important that you do the work yourself!

Q & A

Discord

We will be using Discord (https://discord.gg/Jz8pX45g) for class discussions. The system is designed so that you can get help quickly and efficiently from your classmates and myself. Rather than emailing your questions, I invite you to post them on Discord. The TA and I will check your questions regularly and respond to them. You can post private questions to us or post your questions to the class anonymously (in this case your identity will only be visible to me). It is preferable to post your questions publicly so that everyone can see them and contribute to the discussion.

Office Hours

The TA for this course is Xinyu Yuan and Zhihao Zhan. We will have flexible office hours. If you would like to meet with me or the TA to discuss your questions, you only need to send us a private message on Discord. We will then schedule a meeting at a convenient time. It is preferable to first post your questions on Discord, and then arrange a meeting if necessary.

References

  1. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome, 2009 [ESL]
  2. Deep Learning. Ian Goodfellow, Yoshua Bengio and, Aaron Courville. [DL]
  3. Reinforcement Learning : An Introduction Hardcover. Richard S. Sutton, Andrew G. Barto. A Bradford Book. 2nd edition [RL-Sutton-Barto]
  4. Machine Learning. Kevin Murphy. MIT Press. 2012. [ML-Murphy]
  5. Recommender Systems Handbook, Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. 2011. [RSH]
  6. Data Algorithms : Recipes for Scaling Up with Hadoop and Spark 1st Edition. Mahmoud Parsian. O'Reilly. 2015 [DA]
  7. Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython. Wes McKinney. O'Reilly. 2012 [PDA]
  8. Pattern Recognition and Machine Learning. Christopher Bishop. 2006 [PRML]
  9. Advanced Analytics with Spark. O'Reilly. Second Edition. 2017

Acknowledgement

I thank Prof. Laurent Charlin for sharing his slides and video capsules with me. The majority of the material of this course are based on the previous editions that have been taught by him and Prof. Golnoosh Farnadi. I also thank Prof. Dena Firoozi for sharing her design of this course website.