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: TBD
- 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
| Date | Topic (lectures) | Labs | Projects |
|---|---|---|---|
| 09/9 | Introduction, Linear Regression | ||
| 10/9 | — | Lab 1 | |
| 16/9 | Optimization | ||
| 17/9 | — | 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 | — | 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 | — | 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 |
Full, live schedule with all links is maintained on the course page and GitHub.
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