Turning Software Engineers into Machine Learning Engineers

Alexander Schiendorfer, Carola Gajek, Wolfgang Reif
Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 141:36-41, 2021.

Abstract

A first challenge in teaching machine learning to software engineering and computer science students consists of changing the methodology from a constructive design-first perspective to an empirical one, focusing on proper experimental work. On the other hand, students nowadays can make significant progress using existing scripts and powerful (deep) learning frameworks – focusing on established use cases such as vision tasks. To tackle problems in novel application domains, a clean methodological style is indispensable. Additionally, for deep learning, familiarity with gradient dynamics is crucial to understand deeper models. Consequently, we present three exercises that build upon each other to achieve these goals. These exercises are validated experimentally in a master’s level course for software engineers.

Cite this Paper

BibTeX
@InProceedings{pmlr-v141-schiendorfer21a, title = {Turning Software Engineers into Machine Learning Engineers}, author = {Schiendorfer, Alexander and Gajek, Carola and Reif, Wolfgang}, booktitle = {Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {36--41}, 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/schiendorfer21a/schiendorfer21a.pdf}, url = {https://proceedings.mlr.press/v141/schiendorfer21a.html}, abstract = {A first challenge in teaching machine learning to software engineering and computer science students consists of changing the methodology from a constructive design-first perspective to an empirical one, focusing on proper experimental work. On the other hand, students nowadays can make significant progress using existing scripts and powerful (deep) learning frameworks – focusing on established use cases such as vision tasks. To tackle problems in novel application domains, a clean methodological style is indispensable. Additionally, for deep learning, familiarity with gradient dynamics is crucial to understand deeper models. Consequently, we present three exercises that build upon each other to achieve these goals. These exercises are validated experimentally in a master’s level course for software engineers.} }
Endnote
%0 Conference Paper %T Turning Software Engineers into Machine Learning Engineers %A Alexander Schiendorfer %A Carola Gajek %A Wolfgang Reif %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-schiendorfer21a %I PMLR %P 36--41 %U https://proceedings.mlr.press/v141/schiendorfer21a.html %V 141 %X A first challenge in teaching machine learning to software engineering and computer science students consists of changing the methodology from a constructive design-first perspective to an empirical one, focusing on proper experimental work. On the other hand, students nowadays can make significant progress using existing scripts and powerful (deep) learning frameworks – focusing on established use cases such as vision tasks. To tackle problems in novel application domains, a clean methodological style is indispensable. Additionally, for deep learning, familiarity with gradient dynamics is crucial to understand deeper models. Consequently, we present three exercises that build upon each other to achieve these goals. These exercises are validated experimentally in a master’s level course for software engineers.
APA
Schiendorfer, A., Gajek, C. & Reif, W.. (2021). Turning Software Engineers into Machine Learning Engineers. Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 141:36-41 Available from https://proceedings.mlr.press/v141/schiendorfer21a.html.

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