# Machine Learning CS-433 ‒ MLO ‐ EPFL

https://www.epfl.ch/labs/mlo/machine-learning-cs-433/

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## In the same section
# Machine Learning CS-433
This course is offered jointly by the [TML](https://people.epfl.ch/nicolas.flammarion) and [MLO](http://mlo.epfl.ch/) groups. Previous year’s website: [ML 2025](https://epfml.github.io/cs433-2025/).   
See [here for the **ML4Science** projects](https://www.epfl.ch/labs/mlo/ml4science/).  
  
_Contact us:_ Use the [discussion forum](https://edstem.org/eu/courses/3637/discussion). You can also email the lead assistants **Gizem Yuce** and **Simin Fan** at epfmlcourse@gmail.com, and CC both instructors.
Instructors: **Nicolas Flammarion** and **Martin Jaggi**  
|  **Teaching Assistants**
  * Alejandro Hernandez Cano
  * Benedikt Edler von Querfuth
  * Fares Fawzi
  * Hantao Zhang
  * Johan Wenckstern
  * Kaustubh Ponkshe
  * Liangze Jiang
  * Mark Rofin
  * Mingqiao Ye
  * Vinko Sabolcec

 |  **Student Assistants**
  * Anna Lavrenko
  * Ananya Gupta
  * Benedek Balla
  * Giacomo Porpiglia
  * Miquel Lopez
  * Nahush Kohle
  * Naser Kazemi
  * Rali Lahlou
  * Semanur Avsar
  * Strahinja Nikolic
  * Tommaso Capone
  * Yinan Hu

 |  
| --- | --- |  
| Lectures  | **Tuesday**  | **16:15 – 18:00**  | in [Rolex Learning Center](http://plan.epfl.ch/?lang=en&room=RLCE1240)**  
**  |  
| --- | --- | --- | --- |  
|   | **Wednesday**  | **10:15 – 12:00**  | in [Rolex Learning Center](http://plan.epfl.ch/?lang=en&room=RLCE1240)  |  
| Exercises  | **Thursday**  | **14:15 – 16:00**  |  Rooms: [CO123](http://plan.epfl.ch/?lang=fr&room=CO123), [INF1](http://plan.epfl.ch/?lang=fr&room=INF1), [INF119](http://plan.epfl.ch/?lang=fr&room=INF119), [INJ218](http://plan.epfl.ch/?lang=fr&room=INJ218), [INM202](http://plan.epfl.ch/?lang=fr&room=INM202), [INR219](http://plan.epfl.ch/?lang=fr&room=INR219)   
(assignment see course info sheet)  |  
|  **Language** :  |   | English  |  
| --- | --- | --- |  
|  **Credits** :  |   | 8 ECTS  |  
For a summary of the logistics of this course, see the [course info sheet here (PDF)](https://github.com/epfml/ML_course/raw/main/lectures/course_info_sheet.pdf).  
(and also here is a link to [official coursebook information](http://edu.epfl.ch/coursebook/en/machine-learning-CS-433)).
### Special Announcements
  * Exam Date: T.B.D. in the January exam session, in SwissTech.
  * The links for the exercises signup and the [discussion forum](https://edstem.org/eu/courses/1605/discussion/). All other materials are here on this page and github.
  * Projects: There will be two _group projects_ during the course.
    * Project 1 is not graded and is due Oct 29th.
    * Project 2 counts 30% and is due Dec 17th.
  * The [videos](https://mediaspace.epfl.ch/channel/CS-433+Machine+learning/55647) of the lectures for each week will be available. Labs and projects will be in Python. See [Lab 1](https://github.com/epfml/ML_course/tree/main/labs/ex01) to get started.
  * Code Repository for Labs, Projects, Lecture notes: [github.com/epfml/ML_course](https://github.com/epfml/ML_course)
  * **the exam is closed book but you are allowed one crib sheet (A4 size paper, both sides can be used); bring a pen and white eraser** ; you find the exams from the past years with solutions here:
    * final exam [2025](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2025.pdf), [2024](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2024.pdf), [2023](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2023.pdf), [2022](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2022.pdf), [2021](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2021.pdf), [2020](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2020.pdf), [2019](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2019.pdf), [2018](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2018.pdf), [2017](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2017.pdf), [2016](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2016.pdf),
    * solutions [2025](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2025-solutions.pdf), [2024](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2024-solutions.pdf), [2023](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2023-solutions.pdf), [2022](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2022-solutions.pdf), [2021](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2021-solutions.pdf), [2020](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2020-solutions.pdf), [2019](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2019-solutions.pdf), [2018](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2018-solutions.pdf), [2017](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2017-solutions.pdf), [2016](https://github.com/epfml/ML_course/raw/main/exam/final-exam-2016-solutions.pdf)


### Detailed Schedule
Lecture notes from each class are made [available on github here](https://github.com/epfml/ML_course/tree/main/lectures), and videos [here on mediaspace](https://mediaspace.epfl.ch/channel/CS-433+Machine+learning/55647).  
| Date  | Topics Covered  | Lectures  | Exercises  | Projects  |  
| --- | --- | --- | --- | --- |  
| 8/9  | Introduction, Linear Regression  |  [01a](https://github.com/epfml/ML_course/raw/main/lectures/01/lecture01a_intro.pdf),[01b](https://github.com/epfml/ML_course/raw/main/lectures/01/lecture01b_regression.pdf)[](https://github.com/epfml/ML_course/raw/main/lectures/01/lecture01d_loss_functions.pdf)  |   |   |  
| 9/9  | Loss functions  | [01c](https://github.com/epfml/ML_course/raw/main/lectures/01/lecture01c_loss_functions.pdf)  | [Lab 1](https://github.com/epfml/ML_course/tree/main/labs/ex01)  |   |  
| 15/9  | Optimization  | [02a](https://github.com/epfml/ML_course/raw/main/lectures/02/lecture02a_optimization.pdf)  |   |   |  
| 16/9  | Optimization  |   | [Lab 2](https://github.com/epfml/ML_course/tree/main/labs/ex02)  | [Project 1 start](https://github.com/epfml/ML_course/raw/main/projects/project1/project1_description.pdf)  |  
| 22/9  | Least Squares, Overfitting  |  [03a](https://github.com/epfml/ML_course/raw/main/lectures/03/lecture03a_least_squares.pdf),[03b](https://github.com/epfml/ML_course/raw/main/lectures/03/lecture03b_overfitting.pdf)  |   |   |  
| 23/9  | Max Likelihood, Ridge Regression, Lasso  |  [03c](https://github.com/epfml/ML_course/raw/main/lectures/03/lecture03c_maximum_likelihood.pdf),[03d](https://github.com/epfml/ML_course/raw/main/lectures/03/lecture03d_ridge.pdf)  | [Lab 3](https://github.com/epfml/ML_course/tree/main/labs/ex03)  |   |  
| 29/9  | Generalization, Model Selection, and Validation  |   |   |   |  
| 30/9  | Bias-Variance decomposition  |  [04a](https://github.com/epfml/ML_course/blob/main/lectures/04/lecture04a.pdf), [04b](https://github.com/epfml/ML_course/blob/main/lectures/04/lecture04b.pdf)  | [Lab 4](https://github.com/epfml/ML_course/tree/main/labs/ex04)  |   |  
| 6/10  | Classification  |   |   |   |  
| 7/10  | Logistic Regression  |   | Lab 5  |   |  
| 13/10  | Support Vector Machines  |   |   |   |  
| 14/10  | K-Nearest Neighbor  |   | Lab 6  |   |  
| 27/10  | Kernel Regression  |   |   |   |  
| 28/10  | Neural Networks – Basics, Representation Power  |   | Lab 7  |  Proj. 1 due 29.10.  |  
| 03/11  | Neural Networks – Backpropagation, Activation Functions  |   |   | Project 2 start  |  
| 04/11  | Neural Networks – CNNs, Regularization, Data Augmentation, Dropout  |   | Lab 8  |   |  
| 10/11  | Neural Networks – Transformers  |   |   |   |  
| 11/11  | Adversarial ML  |   | Lab 9  |   |  
| 17/11  | Ethics and Fairness in ML  |   |   |   |  
| 18/11  | Unsupervised Learning, K-Means, Gaussian Mixture Models  |   | Lab 10  |   |  
| 24/11  | Gaussian Mixture Models, EM algorithm  |   |   |   |  
| 25/11  | Matrix Factorizations  |   | Lab 11 & Project Q&A  |   |  
| 01/12  | Text Representation Learning  |   |   |   |  
| 02/12  | LLMs  |   | Lab 12 & Project Q&A  |   |  
| 8/12  | LLMs, Self-supervised Learning  |   |   |   |  
| 9/12  | GANs + Diffusion models  |   | Lab 13  |   |  
| 15/12  | Guest lecture, Edouard Grave: “Audio/Speech LM”  |   |   |   |  
| 16/12  | _Projects pitch session (optional)_  |   |   | Proj. 2 due 17.12.  |  
### Textbooks
_(not mandatory)_
Gilbert Strang,  _Linear Algebra and Learning from Data_  
Christopher Bishop, _Pattern Recognition and Machine Learning  
_ Shai Shalev-Shwartz, Shai Ben-David _, Understanding Machine Learning_  
Michael Nielsen, _Neural Networks and Deep Learning_
### Projects & ML4Science
Projects are done either in [ML4Science in collaboration with any lab of EPFL, or any other academic institution](https://github.com/epfml/ML_course/raw/main/projects/project2/project2_description.pdf).
All [info about the interdisciplinary ML4Science](https://www.epfl.ch/labs/mlo/ml4science/) projects is available on the separate page here.
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