Teaching Machine Learning in 2020

Peter Steinbach, Heidi Seibold, Oliver Guhr
Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 141:1-6, 2021.

Abstract

Faced by the abundant use of machine learning in industry and academia, the effective and efficient teaching of core concepts in this field becomes of high importance. For this, we organized a workshop on teaching methods in the field of machine learning. In this document, we summarize the current standing of the community as by our workshop and their methods. We touch on existing working concepts in machine learning didactics, what methods present initiatives use and cover open teaching resources available to date. With this, we hope to provide a starting point for future collaborations on this central topic given the expanding use of machine learning in science, industry and our daily lives.

Cite this Paper


BibTeX
@InProceedings{pmlr-v141-steinbach21a, title = {Teaching Machine Learning in 2020}, author = {Steinbach, Peter and Seibold, Heidi and Guhr, Oliver}, booktitle = {Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {1--6}, year = {2021}, editor = {Bischl, Bernd and Guhr, Oliver and Seibold, Heidi and Steinbach, Peter}, volume = {141}, series = {Proceedings of Machine Learning Research}, month = {14 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v141/steinbach21a/steinbach21a.pdf}, url = { http://proceedings.mlr.press/v141/steinbach21a.html }, abstract = {Faced by the abundant use of machine learning in industry and academia, the effective and efficient teaching of core concepts in this field becomes of high importance. For this, we organized a workshop on teaching methods in the field of machine learning. In this document, we summarize the current standing of the community as by our workshop and their methods. We touch on existing working concepts in machine learning didactics, what methods present initiatives use and cover open teaching resources available to date. With this, we hope to provide a starting point for future collaborations on this central topic given the expanding use of machine learning in science, industry and our daily lives.} }
Endnote
%0 Conference Paper %T Teaching Machine Learning in 2020 %A Peter Steinbach %A Heidi Seibold %A Oliver Guhr %B Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop %C Proceedings of Machine Learning Research %D 2021 %E Bernd Bischl %E Oliver Guhr %E Heidi Seibold %E Peter Steinbach %F pmlr-v141-steinbach21a %I PMLR %P 1--6 %U http://proceedings.mlr.press/v141/steinbach21a.html %V 141 %X Faced by the abundant use of machine learning in industry and academia, the effective and efficient teaching of core concepts in this field becomes of high importance. For this, we organized a workshop on teaching methods in the field of machine learning. In this document, we summarize the current standing of the community as by our workshop and their methods. We touch on existing working concepts in machine learning didactics, what methods present initiatives use and cover open teaching resources available to date. With this, we hope to provide a starting point for future collaborations on this central topic given the expanding use of machine learning in science, industry and our daily lives.
APA
Steinbach, P., Seibold, H. & Guhr, O.. (2021). Teaching Machine Learning in 2020. Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 141:1-6 Available from http://proceedings.mlr.press/v141/steinbach21a.html .

Related Material