PY 580 Machine Learning for Physicists. Spring 2022


This is the website for PY 580, Machine Learning for Physicists This website will be updated with HWs and suggested readings. In the fall, the class will be based on our review A high-bias, low-variance introduction to Machine Learning for physicsits. The review can be downloaded from Physics Reports or the arXiv if for some reason you prefer that formatting. The Jupyter Notebooks can be downloaded from Github.

General course information: TTh 9:30-11:00. SCI 328/SCI 352 (no Zoom).

Installing Python:

The easiest way to install Python is using the Anaconda distribution: Anaconda.
Please also install Jupyter Notebooks and/or Jupyter Lab: Jupyter.

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: Chapter 1-4/ Notebook 1-2.

Other Viewing + Readings: Lectures 1-3 from Learning from Data. Chapters 1-3 of Information Theory, Inference, and Learning Algorithms. IPython Cookbook fourth feature recipe: Introduction to Machine Learning in Python with scikit-learn. Due date Feb. 1st.

Weeks 2-3: Chapter 5-6/ Notebook 3+4.

Reading for Feb 15-17th: Chapter 6.
HW: Notebooks 3+4 due Feb. 22nd

Weeks 4: Chapter 7/ Notebook 3+4.

Reading for Feb 24th: Chapter 7.
HW: Notebook 5-7 due March 3rd

Weeks 5: Chapter 9

Reading for March 1-3rd: Chapter 9.
HW: Notebook 11 due March 17th

Weeks 6: Chapter 10-11

Reading for March 15-17th: Chapter 10-11.
HW: Notebook 12-13 due March 24th

Weeks 7: Chapter 12+ Paper

Reading for March March 22nd: MLP Mixer. Look at associated code. Many of training procedures are inspired by the Timm library in Pytorch. Read about it here or on its Github repository.
Look at these slides giving overview of modern supervised learning methods. slides
Reading for March March 24th: Chapter 12.
HW: Notebook 12-13 due March 24th

Weeks 8+9: Chapter 13+14

Please read clustering and dimensional reduction HW: Notebook 15 Clustering due Apri 7

Weeks 10: Group Project Planning

We will spend this week in class planning our group projects. Please fill out the following worksheet and return it to me on Tuesday April 11th. Worksheet: pdf or Word
HW: Notebook 15 Clustering due Apri 7