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Course

PHYS-467: Machine learning for physicists

Summary

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.

Lectures

Instructor

Lenka Zdeborová

Lenka Zdeborová is a Professor of Physics and of Computer Science in École Polytechnique Fédérale de Lausanne where she leads the Statistical Physics of Computation Laboratory. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director's Postdoctoral Fellow. Between 2010 and 2020 she was a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2021 the Gibbs lectureship of AMS. She is an editorial board member for Journal of Physics A, Physical Review E, Physical Review X, SIMODS, Machine Learning: Science and Technology, and Information and Inference. Lenka's expertise is in applications of concepts from statistical physics, such as advanced mean field methods, replica method and related message-passing algorithms, to problems in machine learning, signal processing, inference and optimization. She enjoys erasing the boundaries between theoretical physics, mathematics and computer science.

Official source

https://edu.epfl.ch/coursebook/en/machine-learning-for-physicists-PHYS-467

Moodle Page

http://go.epfl.ch/PHYS-467

Summary

Lectures

Instructor

Lenka Zdeborová

Official source

https://edu.epfl.ch/coursebook/en/machine-learning-for-physicists-PHYS-467

Moodle Page

http://go.epfl.ch/PHYS-467

Lectures in this course (130)

Introduction to Image Classification Log in to Mediaspace to watch this video

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Linear Regression: Basics and Estimation Log in to Mediaspace to watch this video

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Regression Again: Exercise 3.1 Log in to Mediaspace to watch this video

Covers exercises on regression, including linear and polynomial regression, high dimensions, and real data analysis.

Inference: Exercise 3.2 Log in to Mediaspace to watch this video

Covers the topic of likelihood estimation and maximum likelihood estimation in inference.

Sparse Regression Log in to Mediaspace to watch this video

Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.

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