Teaching

Convex optimization @Computation and Modelling summer school,WUST 2022

• intro slides

• exercises (updated every day)

• notebook 1 (Introduction to gradient descent on least squares)

• notebook 2 (Gradient descent on generic functions)

• notebook 3 (Non smooth optimization: the proximal gradient algorithm)

• notebook 4 (Alternatives to gradient descent)

Resources

My colleague Pierre Ablin and I have created a repository with some Python advice for our students: https://github.com/pierreablin/python-sessions.

Classes taught

Since 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

• Analysis and Probabilities (MDI 113/114, Bachelor, 10 h)

• Machine Learning and Data Mining (MDI 343, Executive Master, 20 h)

• Linear Models (SD 204, Master, 10 h)

• Practical Machine Learning (SD 207, Master, 10 h)

• Tools and applications for signals and images (SI 101, Bachelor, 6 h)