MVA and MscAI masters, and CentraleSupelec cursus, January-March 2025
News : (reload for fresher news...)
- Link to hand in TP6 is available
- Link to hand in TP5 is available
- HTML course updated and AutoML slides added
- Exam location updated
- TP6 is available
- link to hand in TP4
- link to hand in TP3
- 2025 schedule re-re-updated
- invitation link to the Discord forum for those who did not join in time
- registration for 2025 is open
- course validation updated (NB: includes practical sessions + a final exam) The previous course (January-March 2024) can be found there; this course follows more or less the same lines, though not exactly.
To attend the course, please register here first.
If you had already registered but did not join the Discord forum in time: click here.
NB: Auditors (auditeurs libres) are welcome; just subscribe as well (no need to ask me by email).
Requirements : having already followed a course about neural networks (this is an advanced deep learning course).
Typical mathematical notions used: differential calculus, Bayesian statistics, analysis, information theory.
Teaching team :
Most lectures: Guillaume Charpiat
Practical sessions: Styliani Douka, Rémy Hosseinkhan, Cyriaque Rousselot and Théo Rudkiewicz (incl. materials by Victor Berger, Alessandro Bucci, Loris Felardos, Wenzhuo Liu, Matthieu Nastorg, Francesco Pezzicoli and Antoine Szatkownik)
Course validation :
4 practicals (notebooks), presented in class, to do at home in groups of 1-3 people, and to hand in within 2 weeks
1 final exam (on paper, in class, alone)
Schedule :Note that the schedules are irregular and that locations vary.
Sessions take place on Thursdays (usually), 9h - 12h15 (3 hours course + a 15 minute break), at various places, mostly at CentraleSupelec:
Session 2 : Interpretability (part 1: visualization and analysis) → Practical session: visualization with grad-CAM: notebook and test images →Link to hand in TP2 (password given in the registration email) →[2023]Course notes (pdf) (handwritten with drawings) and lesson summary (html) →[2023] Video recording: part 1 [500MB], part 2 [700MB]
Session 4 : Interpretability (part 2: biases and issues with datasets) →[2023]Course notes (pdf) (handwritten with drawings) and lesson summary (html) →[2023] Video recording: part 3 [700MB], part 4 [400MB]
Ressources: links to introduction to python, numpy, classical machine learning + online deep learning courses
Internship offers :
ours:
Links between explainability, frugal AI, robustness and formal proofs of neural networks: looking for statistically-meaningful concepts and enhancing them. In collaboration with PEPR IA SAIF. Contact me for more details!