Turning Software Engineers into Machine Learning EngineersDownload PDF


Jul 08, 2020 (edited Sep 24, 2020)ECMLPKDD 2020 Workshop TeachML Blind SubmissionReaders: Everyone
  • 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.
  • Keywords: teaching, backpropagation, methodology
  • TL;DR: We summarize our experiences with a software engineering class. Focus on experimental methodology and sharpen the understanding of backprop. Code examples included!
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