Martin Jaggi

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

Group Webpage:

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

+41 21 693 70 59
EPFL > IC > IC-SIN > SIN-ENS

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

+41 21 693 70 59
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

Teaching & PhD

Teaching

Computer Science

Communication Systems

PhD Programs

Doctoral program in computer and communication sciences

PhD Students

Cordonnier Jean-Baptiste Francis Marie Juliette, Gupta Prakhar, He Lie, Karimi Mahabadi Rabeeh, Karimireddy Sai Praneeth Reddy, Koloskova Anastasiia, Lin Tao, Mohtashami Amirkeivan, Pagliardini Matteo, Vogels Thijs,

Past EPFL PhD Students

Drumond Lages De Oliveira Mario Paulo ,

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.

    EECS Seminar: Advanced Topics in Machine Learning

    Students learn about advanced topics in machine learning, artificial intelligence, optimization, and data science. Students also learn to interact with scientific work, analyze and understand strengths and weaknesses of scientific arguments of both theoretical and experimental results.