Machine Learning I: Large-Scale Data Analysis and Decision Making
MATH 60629

Fall 2026

[Schedule]  [Evaluations]  [References]  [Fall 2019]  [Français] 

Instructor: Laurent Charlin

Class Schedule:

Day/Time	Room
Wednesday 12:00pm--3:00pm	Decelles, Natashquan

Office hours: By appointment (email me)

Description:
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) 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 (HEC CAM) below:

1. HEC CAM offers introductory python courses in September. You can register here: CAMS registration.
2. 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. 08/26. Class introduction and math review. [slides]
   • Required reading: Prologue to The Master Algorithm
   • Suggested reading: Chapter 1 of ESL
   • Math review (if needed): have a look at the resources page.
2. 09/02. Machine learning fundamentals
   • Required readings: Chapter 5 of Deep Learning (the book). You can skim 5.4 (except 5.4.4) to 5.10.
   • Capsules: [slides]
     1. Learning Problem [14:40]
     2. Types of Experiences [13:15]
     3. A first Supervised Model [8:03]
     4. Model Evaluation [15:26]
     5. Regularization [4:09]
     6. Model Validation [3:08]
     7. Bias / Variance tradeoff [11:50]
   • In-class material:
     • Summary
     • Exercises (colab)
3. 09/09. Supervised learning algorithms
   • References:
     Sections 4.1-4.3, 4.5 of The Elements of Statistical Learning (available online),
     Sections 3.5 and 4.2 of Machine Learning (K. Murphy)
   • Capsules: [slides]
     1. Nearest Neighbor [19:05]
     2. Linear Classification [15:26]
     3. Introduction to Probabilistic Models (for Classification) [11:55]
     4. The Naive Bayes Model [24:28]
     5. Naive Bayes Example [9:26]
   • In-class material:
     • Summary
4. 09/16. Python for scientific computations and machine learning [Practical Session]
   • The tutorial that you will follow will be available here.
   • I encourage you to start the tutorial ahead of time and to finish it during our 180 minutes together.
5. 09/23. Neural networks and deep learning
   • Required readings: Sections 6.1--6.3 and 6.5 (stop at 6.5.4) of Deep Learning (the book).
   • Other reference: Chapter 11 of the Elements of Statistical Learning (available online).
   • Capsules: [slides]
     1. From linear classification to neural networks [19:28]
     2. Training neural networks [20:14]
     3. Learning representations [13:40]
     4. Neural networks hyperparameters [25:20]
     5. Neural networks takeaways [7:00]
   • In-class exercises:
     • Summary
6. 09/30. No class (National Day for Truth and Reconciliation)
7. 10/07. Recurrent Neural networks and Convolutional neural networks
   • Required readings: Sections 10, 10.1, 10.2 (skim 10.2.2, skip 10.2.3), and 10.7. Sections 9, 9.1, 9.2, 9.3 (9.11 for fun). Both from Deep Learning (the book).
   • Capsules: [slides]
     1. Modelling Sequential Data [8:42]
     2. Practical Overview of RNNs [29:32]
     3. RNNs for language modelling [15:13]
     4. Overview of CNNs [13:30]
     5. Convolutions and Pooling [26:00]
     6. Conclusions and Practical remarks [9:17]
   • In-class material:
     • Summary
8. 10/14. Unsupervised learning
   • Required reading: Section 14.3 (skip 14.3.5 and 14.3.12) of the Elements of Statistical Learning.
   • Capsules: [slides]
     1. Introduction to unsupervised learning [8:17]
     2. K-means clustering [41:58] (there's a natural break at 22:28)
     3. GMMs for clustering [17:52]
     4. Beyond Clustering [14:42]
   • In-class material:
     • Summary
9. 10/21. Reading week (no class)
10. 10/28. Project team meetings (online)
11. 11/04 Attention and the Transformer architecture [slides]
    • Will be given in class.
    • Complementary reading: An Introduction to Transformers
12. 11/11 Transformers in practice [slides]
    • Will be given in class.
13. 11/18 Recommender systems
    • Required preparation for the case: Case Presentation and class execution (answer to Question 1 must be submitted no later than the day before class)
      • Class slides
14. 11/25 Modern generative models
    • Will be given in class.
    • Slides
15. 12/02 Class project presentations

Evaluations

1. Homework (15%)
   • Available early October.
2. Project (30%)
   • Due date: study plan end of October, final report mid-December (exact dates to be confirmed).
   • Instructions
3. Project presentation (10%)
   • During the last class (December 2)
   • Instructions
4. Final Exam (30%)
   • Date: December 9 (Wednesday), Time: 1:30pm--4:30pm, Room: TBD.
   • 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. Quizzes (15%)
   • Short quizzes (5--10 minutes) will take place at the beginning of class, roughly weekly (six quizzes in total).
   • Your five best results will count toward your final grade (3% each).

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