We created a new regular course Physics 361 – Machine Learning in Physics, which was taught for the first time in Spring 2024 by Münchmeyer and Shiu. We expect that this course will be offered every spring semester.

This course summarizes the many ways in which Machine Learning is used in physics, such as classification, regression, simulations with generative models, likelihood-free inference and uncertainty quantification. We expect to update and improve the course at each iteration, to keep up with the rapid development in the field.


Lecture Slides (Spring 2024)


Background: Probability theory and Information theory

Basics of Machine Learning

Basic Neural Network Architectures

Unsupervised methods

Guest lectures

Decision Trees, KNN, Random Forrests, Scikit-learn

Transformers, LLMs

Reinforcement Learning

Simulation-based inference and Normalizing flows

Diffusion Models

Solving Inverse Problems and PDEs with NN

Final Lecture

  • Lecture 28 – Presentation of Final Projects