Keep the gradient flowing


Series of blog posts on Polynomials and Optimization (2020--)

Series of blog posts on the Frank-Wolfe algorithm (2018--)

Random Matrix Theory and Machine Learning (2021)

Courtney Paquette, Jeffrey Pennington, Tom Trogdon and I gave a tutorial at the ICML 2021 conference on the applications of random matrix theory to machine learning.

All course materials can be found in the website

A birds-eye view of optimization algorithms (2018)

"A birds-eye view of optimization algorithms". Material for a 1.5h introduction to optimization algorithms given in 2018.

Data science course, UC Berkeley (2017)

Technological advances in data gathering and storage have led to a rapid proliferation of large amounts of data in diverse areas such as climate studies, cosmology, medicine, Web data processing, and engineering. Making sense of this data deluge requires a set of skills which have become fundamental in any major corporation and any almost any scientific discipline.

Instructors: Fabian Pedregosa, Laurent El Ghaoui, Bowen Yin Wang (Teaching Assistant).

2016-2017: Distributed & Stochastic Optimization for Machine Learning

How can we tune the parameters of a logistic regression model using terabytes of data, when most machines nowadays only have a few dozens or at most a few hundreds of gigabytes of RAM?

The goal of this course will be to familiarize students with algorithmic tools that are now crucial to run machine learning algorithms at scale: namely stochastic, incremental, distributed and asynchronous optimization. We will cover these tools both in theory, to study their convergence properties, and in practice, through code exercises.

Introduction to Python and scikit-learn (2016-2017)

Master M2: Mathématiques, Apprentissage et Sciences Humaines (MASH)

The goal of this course is to develop experience in machine learning through real world data science challenges.