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

* Examples and types of problems that machine learning can solve. * Linear regression in matrix notation. The concept of prediction, estimation. Least squares method. High-dimensional underdetermined problems and the concept of regularization aka ridge regression. Polynomial regression. The concept of bias and variance trade-off and overfitting. Usage of train, validation and test sets.

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.