Machine Learning (CS-433)
Previous year’s website: ML 2024.
Contact: Use the discussion forum.
Instructors: Bob West
Logistics
- Lectures
- Tue 16:15–18:00 — Rolex Learning Center
- Wed 10:15–12:00 — Rolex Learning Center
- Exercises Thu 14:15–16:00 Rooms: INF1, INF119, INJ218, INM202, CO123, INR219
- Language: English
- Credits: 8 ECTS
- Info sheet: Course info
- Official catalog: EPFL coursebook
Special Announcements
- Exam: Thursday, 15 January 2026, 15h15 - 18h15
- Projects: two group projects
- Project 1 — 10%, due October 31.
- Project 2 — 30%, due December 18.
- Videos: weekly lecture videos on Mediaspace.
- Code & material: epfml/ML_course.
- Exam format: closed book; one A4 crib sheet (both sides).
Past exams (+ solutions): 2016–2023.
Schedule & Materials
Note that this schedule is only approximative. We’ll go through the material in the order listed here, but it might well be that certain materials will be reached earlier or later than listed here.
Slides will be made available on GitHub.
| Date | Topic (lectures) | Labs | Projects |
|---|---|---|---|
| 09/9 | Introduction, Regression, Linear Regression | ||
| 10/9 | Loss Functions, Optimization | Lab 1 | |
| 16/9 | (cont’d) | ||
| 17/9 | (cont’d) | Lab 2 | Project 1 start |
| 23/9 | Least Squares, Overfitting | ||
| 24/9 | ML Estimation, Ridge, Lasso | Lab 3 | |
| 30/09 | Generalization & Model Selection | ||
| 1/10 | (cont’d) | Lab 4 | |
| 7/10 | Classification | ||
| 8/10 | Logistic Regression | Lab 5 | |
| 14/10 | SVMs | ||
| 15/10 | KNN | Lab 6 | |
| 28/10 | Kernel Regression | ||
| 29/10 | Neural Nets: Basics & Rep. Power | Lab 7 | |
| 31/10 | Project 1 due | ||
| 04/11 | Backprop & Activations | Project 2 start | |
| 05/11 | CNNs, Regularization, Augmentation, Dropout | Lab 8 | |
| 11/11 | Transformers | ||
| 12/11 | Adversarial ML | Lab 9 | |
| 18/11 | Ethics & Fairness in ML | ||
| 19/11 | Unsupervised: K-Means, GMMs | Lab 10 | |
| 25/11 | GMMs, EM | ||
| 26/11 | Matrix Factorization | Lab 11 | Project Q&A |
| 02/12 | Text Representation Learning | ||
| 03/12 | (cont’d) | Lab 12 | Project Q&A |
| 09/12 | LLMs, Self-Supervised Learning | ||
| 10/12 | GANs + Diffusion | Lab 13 | |
| 16/12 | Guest: TBD | ||
| 17/12 | Projects pitch (optional) | Project 2 due 18/12 |
Textbooks (optional)
- 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
Teaching Team
Teaching Assistants
Alexander Hägele • Atli Kosson • Dongyang Fan • Francesco D’Angelo • Gizem Yüce • Hristo Papazov • Oguz Yüksel • Bettina Messmer • Diba Hashemi • Justin Samuel Deschenaux • Mohammad Hossein Amani • Saibo Geng
Student Assistants
Adam Mesbahi Amrani • Ahyoung Seo • Efe Tarhan • Eren Akçanal • Francesco Bellotto • Igor Pavlović • Jacques Mandriota • Jingxuan Sun • Leonardo Martella • Michele Lanfranconi • Mikulas Vanousek • N’Zian Cédric Koffi • Shunchang Liu • Vincenzo Sigillo’ Massara • Xinran Li • Yassine Mustapha Wahidy • Zhuofu Zhou