Lenka Zdeborová

EPFL SB IPHYS SPOC1
BSP 722 (Cubotron UNIL)
Rte de la Sorge
1015 Lausanne

Web site:  Web site:  https://www.epfl.ch/labs/spoc/

EPFL IC IINFCOM SPOC2
BC 405 (Bâtiment BC)
Station 14
1015 Lausanne

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

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

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

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Administrative data

Teaching & PhD

Teaching

Physics

Computer Science
Communication Systems

Courses

Data analysis for Physics

This lecture will introduce the basics of data analysis and learning from data, error estimation and stochasticity in physics. Concepts will be introduced theoretically as well as via numerical exercises done in Python.

Machine learning for physicists

Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised.

Statistical physics of computation

This course covers the statistical physics approach to computer science problems ranging from graph theory and constraint satisfaction to inference and machine learning. In particular the replica and cavity methods, message passings algorithms, and analysis of the related phase transitions.

Statistical physics for optimization & learning

This course covers the statistical physics approach to computer science problems, with an emphasis on heuristic & rigorous mathematical technics, ranging from graph theory and constraint satisfaction to inference to machine learning, neural networks and statitics.

Lecture series on scientific machine learning

This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the very scientific method.