Teaching Resources

A3D3 members have a range of experience in teaching machine learning courses to a wide education audience. These courses are increasingly drawing on A3D3 research materials for illustrative examples or problem sets. By making these course materials available to the community, we hope to help those educators who are designing or implementing similar courses from the start or looking to update existing courses with fresh material. Queries about individual courses listed below should be addressed to the named course developer.

PHYS 503: Instrumental Physics Applications of Machine Learning

Designed to give students a solid foundation in machine learning applications to physics, positioning itself at the intersection of machine learning and data-intensive science. This course will introduce students to the fundamentals of analysis and interpretation of scientific data, and applications of machine learning to problems common in laboratory science such as classification and regression. There will be two 75-minute classes each week, split into discussions of core principles and hands-on exercises involving coding and data. There will be several projects throughout semester that will build on the course material and utilize open source software and open data in physics and related fields. The list of topics will evolve, according to the interests of the class and instructors. Material will be clustered into units of varying duration, as indicated below. The lists of suggested readings and references are advisory; a large amount of material of excellent quality is now available.

PHYS 460: Machine Learning for Physics

This course presents an introduction to modern data science, artificial intelligence (AI) and machine learning (ML) from a physics perspective. Students learn the basic concepts, tools, and methods of AI/ML applied to scientific challenges. They study how physics knowledge can be incorporated into AI/ML models to improve their learning efficiency, performance, and interpretability. Students also learn how to learn from the machines, including methods for AI explainability and uncertainty quantification. Students explore the different types of learning from data, including supervised, semi-supervised and unsupervised learning. Topics covered include artificial neural networks (NNs), AI/ML-enhanced modeling/simulation, deep generative models, simulation-free inference, variational inference, convolutional NNs, recursive NNs, geometric deep learning, attention mechanism and transformers, auto-encoders, and anomaly detection. Applications to physics will be emphasized, particularly through projects using open scientific data.

PHYS 350: Data Analysis for Physics

This course presents a basic introduction to probability and data analysis from a physics perspective. The methods of extracting meaningful information from data using probability theory and statistical analyses will be presented. Additionally, students will gain familiarity with the concepts through programming exercises using Python notebooks. Topics to be covered include: basics of statistics and probability theory, probability distributions, estimators, uncertainties, confidence intervals and hypothesis testing, Fourier and Monte Carlo methods.