MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept
Michal Tkáč Jakub Sieber Lara Kuhlmann Matthias Brueggenolte Alexandru Rinciog Michael Henke Artur M. Schweidtmann Qinghe Gao Maximilian F. Theisen Radwa El Shawi
Machine Learning (ML) techniques are encountered nowadays across disciplines,
from social sciences, through natural sciences to engineering. The broad
application of ML and the accelerated pace of its evolution lead to an
increasing need for dedicated teaching concepts aimed at making the application
of this technology more reliable and responsible. However, teaching ML is a
daunting task. Aside from the methodological complexity of ML algorithms, both
with respect to theory and implementation, the interdisciplinary and empirical
nature of the field need to be taken into consideration. This paper introduces
the MachineLearnAthon format, an innovative didactic concept designed to be
inclusive for students of different disciplines with heterogeneous levels of
mathematics, programming and domain expertise. At the heart of the concept lie
ML challenges, which make use of industrial data sets to solve real-world
problems. These cover the entire ML pipeline, promoting data literacy and
practical skills, from data preparation, through deployment, to evaluation.