PY 580 Machine Learning for Physicists. Fall 2025

This is the website for PY 580, Machine Learning for Physicists This website will be updated with HWs and suggested readings. The class will start by covering the first half of our review: A high-bias, low-variance introduction to Machine Learning for physicsts. The review can be downloaded from Physics Reports. The Jupyter Notebooks can be downloaded from Github. In the second half of the class, we will pivot to to more advanced/modern topics such as self-supervised learning, large language models, and diffusion models. The goal is to have a "Part II" of the ML review that covers these topics and updates the old review.

General course information: TBD.

Installing Python:

The easiest way to install Python is using the Anaconda distribution: Anaconda.
Please also install Jupyter Notebooks and/or Jupyter Lab: Jupyter. Alternatively, you can run the notebooks on Google Colab. Students are encouraged to code with modern AI coding tools. You can also use the SCC cluster.

Useful Resources:

David Mackay's Information Theory, Inference, and Learning Algorithms.

Abu Mustafa's Online Course: Learning from Data

Michael Neilsen's Neural Networks and Deep Learning

Python Packages: Python scikit-learn Library , PyTorch , TensorFlow

Syllabus, Grading, and Course Information: pdf

Weeks 1-2: Chapter 1-4/ Notebook 1-2.