Machine Learning for Theoretical Physics

Prerequisite

Elementary multivariate calculus, elementary statistics. Some basic General Relativity and Statistical Mechanics may help in following the applications.

Abstract

The course is targeted to those who know beginning graduate level physics but do not know machine learning. We will cover important methods in machine learning with a view to their applications to current physics such as string theory, particle physics, critical phenomena, gravitational waves and integrability. We will also cover some applications to Lie algebras. We will use Python3, scikit-learn and Keras/Tensorflow. These will be introduced in the lectures.

Lecturer Intro.

Dr Shailesh Lal received his PhD from the Harish-Chandra Research Institute. His research interests are applications of machine learning to string theory and mathematical physics, black holes in string theory and higher-spin holography.