Welcome! I am a Visiting Researcher at Facebook AI research (FAIR) in NYC, and an Associate Professor at Télécom Paris (currently on leave). I am interested in randomized and stochastic methods for solving large scale optimization and numerical analysis problems that come from machine learning applications. For more details see my CV.

Cornell lecture: Optimization for Machine Learning (Spring 2020)

In case you need it, here is a short probability revision.
1) Details of the course.
2) Lecture slides for SGD.
3) Exercises on stochastc methods for ridge regression (solutions) and SGD convergence proof (solutions).
4) Lecture slides for Variance Reduction.

Master IASD: AI Systems and Data Science (Fall 2019)

For prerequisites see here . For revision of vector calculus see here .
The course information can be found here
1) Slides on introduction to SGD and ERM
2) Lecture notes on probability revision
3) Exercise list on stochastc methods for ridge regression (solutions)
4) Slides on SGD and variants
5) Exercise on SGD proof (solutions)
6) Python notebook on SGD (solutions)

Telecom Paris IA317: Large scale machine learning (Fall 2019)

The course information can be found here.
For prerequisites and revision material see here.
1) Lecture notes on dimension reduction tools and sparse matrices
2) Exercise list on dimension reduction tools and sparse matrices
3) Python Notebook graded homework on dimension reduction tools and sparse matrices. Data sets needed for homework: colon-cancer, anthracyclineTaxaneChemotherapy and sector.scale.

MDI210 Optimization et Analise númeric (Fall 2020)

The complete lectures notes with examples and exercises (by Irene Charon Olivier Hudry) are here. Here are my notes and slides (WARNING: these are a work in progress!)
1) Lecture notes on numerical linear algebra
2) Lecture notes on linear and nonlinear optimization (Updated 12/10/2020)
3) Slides on Linear systems and Eigenvalues
4) Slides on Linear Programming
5) Slides on Nonlinear Linear Programming (Updated 12/10/2020)

Master2 Optimization for Data Science (Fall 2019)

For prerequisites see here . For revision of vector calculus see here .
Lecture notes on gradient descent proofs.

0) Exercises on convexity and smoothness (solutions)
1) Exercises on complexity and convergence rates (solutions)
2) Lecture I: intro to ML, convexity, smoothness and gradient descent
3) Exercises ridge regression and gradient descent (solutions)
4) Lecture II: proximal gradient methods
5) Exercises on proximal operator (solutions)
6) Lab1: Proximal gradient methods
7) Lecture III: Stochastic gradient descent
8) Exercises on stochastc methods for ridge regression (solutions)
9) Exercise on SGD proof (solutions)
10) Lecture IV: Stochastic variance reduced gradient methods
11) Exercise on variance reduction, proof of convergence of SVRG
12) Lecture V: Sampling and momentum
13) Exercise on sampling and momentum
14) Python notebook on momentum

African Masters of Machine Intelligence (AMMI) (Winter 2019)

1) Lecture I: Introduction into ML and optimization
2) Exercises on convexity, smoothness and gradient descent
3) Lecture II: proximal gradient methods
4) Exercises on proximal operator
5) Lecture III: Stochastic gradient descent
6) Exercises on stochastc methods
7) Lecture IV: Stochastic variance reduced gradient methods
8) Notes on stochastic variance reduced methods

• gowerrobert$@$gmail.com
• Télécom Paris, 19 Place Marguerite Perey, 91120 Palaiseau, France. Office: 5.C45
• email for appointment