Teaching Computational Machine Learning (without Statistics)

Katherine M. Kinnaird
Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 141:17-22, 2021.

Abstract

This paper presents an undergraduate machine learning course that emphasizes algorithmic understanding and programming skills while assuming no statistical training. Emphasizing the development of good habits of mind, this course trains students to be independent machine learning practitioners through an iterative, cyclical framework for teaching concepts while adding increasing depth and nuance. Beginning with unsupervised learning, this course is sequenced as a series of machine learning ideas and concepts with specific algorithms acting as concrete examples. This paper also details course organization including evaluation practices and logistics.

Cite this Paper

BibTeX
@InProceedings{pmlr-v141-kinnaird21a, title = {Teaching Computational Machine Learning (without Statistics)}, author = {Kinnaird, Katherine M.}, booktitle = {Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {17--22}, 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/kinnaird21a/kinnaird21a.pdf}, url = {https://proceedings.mlr.press/v141/kinnaird21a.html}, abstract = {This paper presents an undergraduate machine learning course that emphasizes algorithmic understanding and programming skills while assuming no statistical training. Emphasizing the development of good habits of mind, this course trains students to be independent machine learning practitioners through an iterative, cyclical framework for teaching concepts while adding increasing depth and nuance. Beginning with unsupervised learning, this course is sequenced as a series of machine learning ideas and concepts with specific algorithms acting as concrete examples. This paper also details course organization including evaluation practices and logistics.} }
Endnote
%0 Conference Paper %T Teaching Computational Machine Learning (without Statistics) %A Katherine M. Kinnaird %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-kinnaird21a %I PMLR %P 17--22 %U https://proceedings.mlr.press/v141/kinnaird21a.html %V 141 %X This paper presents an undergraduate machine learning course that emphasizes algorithmic understanding and programming skills while assuming no statistical training. Emphasizing the development of good habits of mind, this course trains students to be independent machine learning practitioners through an iterative, cyclical framework for teaching concepts while adding increasing depth and nuance. Beginning with unsupervised learning, this course is sequenced as a series of machine learning ideas and concepts with specific algorithms acting as concrete examples. This paper also details course organization including evaluation practices and logistics.
APA
Kinnaird, K.M.. (2021). Teaching Computational Machine Learning (without Statistics). Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 141:17-22 Available from https://proceedings.mlr.press/v141/kinnaird21a.html.

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