Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges

Authors

Viviana Acquaviva, CUNY New York City College of TechnologyFollow

Document Type

Working Paper

Publication Date

8-18-2021

Abstract

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.

Comments

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.