Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges
Autor: | Acquaviva, Viviana |
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Rok vydání: | 2021 |
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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 |
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