TeachingMaterial for the two labs can be downloaded from here. Slides are here. Optimization for large scale Machine Learning, M2 ENS 2022-2023 & 2023-2024The goal of the class is to cover theoretical aspects and practical Python implementations of popular optimization algorithms in machine learning, with a focus on modern topics: huge scale models, automatic differentiation, deep learning, implicit bias, etc. Notes for the class in their 2022 version are here and exercises are here Schedule: From November 21st onwards: Tuesday 08 h 00, Friday 13 h 30 (room B1). Validation: weekly theoretical homeworks, labs and paper presentation at the end of the class. Syllabus:
Resources:
OLISSIPO Winter school: dimensionality reduction (02/2023)Convex optimization @Computation and Modelling summer school, WUST 2022
ResourcesMy colleague Pierre Ablin and I have created a repository with some Python advice for our students: https://github.com/pierreablin/python-sessions. Classes taughtSince 2019, I teach the Python for datascience class (42 h per year) 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, and Hicham Janati in 2021. Since 2020 I teach and handle practical sessions and data camps in Ecole Polytechnique's Executive education (70 h). Topics involved dimension reduction, clustering, scaling computations, visualization and datacamp. I designed 2 full python labs with Erwan Le Pennec on these topics. From 2017 to 2019, my main teaching activity was the Optimization for datascience class of the Datascience Master, totalling 2*40 h including 4 h as lecturer. Amongst others, this involved refactoring of the practical sessions, tutoring of students during office hours, and partaking in the design of the final exam. In 2016-2017, I was a TA at Télécom Paris, for
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