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

Viviana Acquaviva
Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 170:35-39, 2022.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v170-acquaviva22a, title = {Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges}, author = {Acquaviva, Viviana}, booktitle = {Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {35--39}, year = {2022}, editor = {Kinnaird, Katherine M. and Steinbach, Peter and Guhr, Oliver}, volume = {170}, series = {Proceedings of Machine Learning Research}, month = {08--13 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v170/acquaviva22a/acquaviva22a.pdf}, url = {https://proceedings.mlr.press/v170/acquaviva22a.html}, 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.} }
Endnote
%0 Conference Paper %T Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges %A Viviana Acquaviva %B Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop %C Proceedings of Machine Learning Research %D 2022 %E Katherine M. Kinnaird %E Peter Steinbach %E Oliver Guhr %F pmlr-v170-acquaviva22a %I PMLR %P 35--39 %U https://proceedings.mlr.press/v170/acquaviva22a.html %V 170 %X 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.
APA
Acquaviva, V.. (2022). Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges. Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 170:35-39 Available from https://proceedings.mlr.press/v170/acquaviva22a.html.

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