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Course

CS-439: Optimization for machine learning

Summary

This course teaches an overview of modern optimization methods, for applications in machine learning and data science. In particular, scalability of algorithms to large datasets will be discussed in theory and in implementation.

Lectures

Instructors (2)

Martin Jaggi

Martin Jaggi is a Tenure Track Assistant Professor at EPFL, heading the Machine Learning and Optimization Laboratory. Before that, he was a post-doctoral researcher at ETH Zurich, at the Simons Institute in Berkeley, and at École Polytechnique in Paris. He has earned his PhD in Machine Learning and Optimization from ETH Zurich in 2011, and a MSc in Mathematics also from ETH Zurich.

Nicolas Henri Bernard Flammarion

Nicolas Flammarion is a tenure-track assistant professor in computer science at EPFL. Prior to that, he was a postdoctoral fellow at UC Berkeley, hosted by Michael I. Jordan. He received his PhD in 2017 from Ecole Normale Superieure in Paris, where he was advised by Alexandre d’Aspremont and Francis Bach. In 2018 he received the prize of the Fondation Mathematique Jacques Hadamard for the best PhD thesis in the field of optimization. His research focuses primarily on learning problems at the interface of machine learning, statistics and optimization.

Official source

https://edu.epfl.ch/coursebook/en/optimization-for-machine-learning-CS-439

Moodle Page

http://go.epfl.ch/CS-439

Summary

Lectures

Instructors (2)

Martin Jaggi

Nicolas Henri Bernard Flammarion

Official source

https://edu.epfl.ch/coursebook/en/optimization-for-machine-learning-CS-439

Moodle Page

http://go.epfl.ch/CS-439

Lectures in this course (12)

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