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)

**Introduction**

- Lecture 1 – Introduction: Overview of Applications of Machine Learning to physics

**Background: Probability theory and Information theory**

**Basics of Machine Learning**

**Basic Neural Network Architectures**

**Unsupervised methods**

**Guest lectures**

- Lecture 13 – GNN: guest lecture by Yurii Kvasiuk on Graph Neural Networks
- Lecture 14 – Autoencoder: guest lecture by Jacky Yip on Autoencoders

**Decision Trees, KNN, Random Forrests, Scikit-learn**

- Lecture 15 – Decision Trees
- Lecture 16 – DT-kNN
- Lecture 17 – random forests
- Lecture 18 – redshift application

**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