Martin Jaggi

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martin.jaggi@epfl.ch +41 21 693 70 59 http://mlo.epfl.ch

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mlo.epfl.ch

EPFL IC IINFCOM MLO
INJ 341 (Bâtiment INJ)
Station 14
CH-1015 Lausanne

+41 21 693 70 59
+41 21 693 52 26
Office: INJ 341
EPFL > IC > IINFCOM > MLO

EPFL IC SIN-GE
INN 112 (Bâtiment INN)
Station 14
CH-1015 Lausanne

+41 21 693 70 59
Office: INN 112
EPFL > IC > IC-SIN > SIN-ENS

Web site: Web site: https://sin.epfl.ch

EPFL IC SSC-GE
INR 130 (Bâtiment INR)
Station 14
CH-1015 Lausanne

+41 21 693 70 59
Office: INR 130
EPFL > IC > IC-SSC > SSC-ENS

Web site: Web site: https://ssc.epfl.ch

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Administrative data

Fields of expertise

Machine Learning, Optimization

Biography

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.

Teaching & PhD

Teaching

Computer Science

Communication Systems

PhD Programs

Doctoral program in computer and communication sciences

PhD Students

Chayti El Mahdi, Cordonnier Jean-Baptiste Francis Marie Juliette, He Lie, Karimi Mahabadi Rabeeh, Koloskova Anastasiia, Lin Tao, Mohtashami Amirkeivan, Pagliardini Matteo, Swamy Vinitra, Vogels Thijs,

Past EPFL PhD Students

Drumond Lages De Oliveira Mario Paulo , Gupta Prakhar , Karimireddy Sai Praneeth Reddy ,

Courses

Machine learning

Machine learning methods are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.

Optimization for machine learning

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. Fundamental Contents:

Convexity, Gradient Methods, Proximal algorithms, Stochastic and Online Variants of mentioned methods, Coordinate Descent Meth

Topics in Machine Learning Systems

This course will cover the latest technologies, platforms and research contributions in the area of machine learning systems. The students will read, review and present papers from recent venues across the systems for ML spectrum.

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