Lenka Zdeborová

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

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

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Administrative data

Teaching & PhD

Teaching

Physics

PhD Programs

Doctoral Program in Physics

Doctoral program in computer and communication sciences

Courses

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

Interest in the methods and concepts of statistical physics is rapidly growing in fields as diverse as theoretical computer science, probability theory, machine learning, discrete mathematics, optimization, signal processing and others. Large part of the related work has relied on the use of message-passing algorithms and their connection to the statistical physics of glasses and spin glasses.

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.