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.
@article{arxiv.2108.08313,
title = {Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges},
author = {Viviana Acquaviva},
journal= {arXiv preprint arXiv:2108.08313},
year = {2021}
}
Comments
Paper to be presented at the "Teaching ML" workshop at the European Conference of Machine Learning 2021. The Conclusions section includes a link to materials