Turning software engineers into machine learning engineers

  • 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.

Download full text files

Export metadata


Number of document requests

Additional Services

Share in Twitter Search Google Scholar
Author:Alexander SchiendorferORCiDGND, Carola GajekORCiDGND, Wolfgang ReifORCiDGND
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/79710
Parent Title (English):Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop at ECML-PKDD 2020, 14 September 2020, virtual conference
Publisher:ML Research Press
Editor:Bernd Bischl, Oliver Guhr, Heidi Seibold, Peter Steinbach
Type:Conference Proceeding
Year of first Publication:2021
Publishing Institution:Universität Augsburg
Release Date:2020/09/25
First Page:36
Last Page:41
Series:PMLR - Proceedings of Machine Learning Research ; 141
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Software & Systems Engineering
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik / Lehrstuhl für Softwaretechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht