Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges
arXiv:2108.08313
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.
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