Publications and Research

Document Type

Working Paper

Publication Date



This paper summarizes some challenges encountered and best practices established in several years of teaching Machine Learning for the Physical Sciences at the undergraduate and graduate level. I discuss motivations for teaching ML to physicists, desirable properties of pedagogical materials, such as accessibility, relevance, and likeness to real-world research problems, and give examples of components of teaching units.


Acquaviva, V. (2021). Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges. arXiv preprint arXiv:2108.08313. Proceedings of the 2nd Teaching in Machine Learning Workshop, PMLR, 2021.

This paper is distributed under a Creative Commons Attribution (CC BY-NC 4.0) License.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.