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

Autor:	Acquaviva, Viviana
Rok vydání:	2021
Předmět:	Physics - Physics Education
Druh dokumentu:	Working Paper
Popis:	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. Comment: 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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2108.08313 Zobrazit plný text záznamu View this record from Arxiv