Mathematical background for Machine Learning
ICMS tutorial on generative models, 2026
Slides, lab to complete, complete training part only, completed version
Slides for the benchopt tutorial
Generative models, M2 ENS 2025-2027
Class details are here.
Computational Optimal Transport for Machine and Deep Learning, M2 ENS 2024-2026
Class details are here.
Optimization for large scale Machine Learning, M2 ENS 2022-2024
Class details are here.
Classes taught
Summer schools:
- ICMS AI Hackathon for women in the mathematical sciences: Generative models (02/2026)
- AI Hub Sénégal: Generative models, with Quentin Bertrand and Rémi Emonet (04/2025)
- OLISSIPO Winter school: dimensionality reduction with Titouan Vayer (02/2023)
- Convex optimization @Computation and Modelling summer school, WUST 2022 (intro slides and exercises)
Since my arrival at ENS de Lyon (Nov. 2021):
- 32 h on Generative models (2025-2027), M2 level, with Quentin Bertrand and Rémi Emonet.
- 32 h on Fundamentals of Machine Learning (2025-2026), L3 level.
- 32 h on optimal transport (2024-2026), M2 level, with Titouan Vayer and Quentin Bertrand.
- 2x20 h on Fundamentals of Machine Learning (2024-2026), M1 level.
- 2x32 h on large scale optimization for machine and deep learning (2022-2024), M2 level.
- 12 h on optimization (2023-2024), M1 level.
I am a part-time teacher at Polytechnique, where I teach the Python for datascience class (42 h per year since 2019) in the X/HEC “Datascience for business” Master, using live coding inspired by the Software Carpentry workshops. I designed the course from scratch, collaborating with Joan Massich in 2019, Quentin Bertrand in 2020, Hicham Janati in 2021, Sylvain Combettes in 2022 and Badr Moufad in 2023.
Since 2020 I teach and handle practical sessions and data camps in Ecole Polytechnique’s Executive education. Topics involved dimension reduction, clustering, scaling computations, visualization and datacamp. I designed 2 full python labs with Erwan Le Pennec on these topics.