Machine Learning for Large-Scale Data Analysis and Decision Making
MATH 60629A

Fall 2022

[Schedule]  [Evaluations]  [References]  [Fall 2019]  [Fran├žais

Instructor: Laurent Charlin

Class Schedule: I am teaching the course twice this term, once in French and once in English.

Day/Time Section Room
Monday 8:30am--11:30am J01 (English) Saine Marketing
Wednesday 8:30am--11:30am J02 (French) Saine marketing

Office hours: Wednesday 1pm--2pm (Room 4.817)

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").
We will survey different machine learning techniques (supervised, unsupervised, reinforcement learning) as well as some applications (e.g., recommender systems) and ways to scale-up computations (e.g., distributed frameworks).

**Course delivery:** This course will be given as a flipped classroom. It is an instructional strategy where students learn the material before they come to 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.

Mathematical Note: Mathematical maturity will be assumed.

Programming Note: Python knowledge will be assumed. If you do not know Python I have listed a few ways to learn the basics below. I recommend option 1 (Data Camp) or option 2 below:

  1. DataCamp. Complete Chapters 1, 2, 3 of the Introduction to Python course. To get access to Chapters 2 and 3 use the link I sent you.
  2. HEC CAM offers introductory python courses in September. Register here: CAM registration
  3. Here is the tutorial we used in 2018: Fall 2018 tutorial. While I think the first two options are superior, this will give you an idea of the level I am expecting. particularly recommend this

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

Weekly Schedule

  1. 08/29. Class introduction and math review. [slides]
  2. 09/12. Machine learning fundamentals
  3. 09/19. Supervised learning algorithms
  4. 09/26. Python for scientific computations and machine learning [Practical Session]
    • The tutorial that you will follow is here (on colab), Solutions.
    • I encourage you to start the tutorial ahead of time and to finish it during our 180 minutes together.
  5. 10/05. Neural networks and deep learning
  6. 10/11. Recurrent Neural networks and Convolutional neural networks
  7. 10/17. Unsupervised learning
  8. 10/24. Reading week (no class)
  9. 10/31. Project team meetings
  10. 11/07. Parallel computational paradigms for large-scale data processing
  11. 11/14 Recommender systems
  12. 11/21 Sequential decision making I
  13. 11/28 Sequential decision making II
  14. 12/05 Class project presentations
    • This class will be help in Room Manuvie (1st floor, blue section)


  1. Homework (20%)
  2. Project (30%)
    • Due date: study plan October 28. Final report December 15 (by the end of the day).
    • Instructions
  3. Project presentation (10%)
  4. Final Exam (30%)
    • Date: December 12, Time: 9:00am-12:00pm,
    • Documentation allowed: cheat sheet (standard size 8.5 x 11, double sided), calculator.
    • Material covered: Everything covered in class + required lectures.
    • Past exam: 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.
    • Post your summaries using this form
    • Deadline: December 17


  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