TY - GEN
T1 - Teaching machine learning to programming novices
T2 - 32nd Interdisciplinary Information Management Talks: Changes to ICT, Management, and Business Processes through AI, IDIMT 2024
AU - Tkáč, Michal
AU - Sieber, Jakub
AU - Meyer, Anne
AU - Kuhlmann, Lara
AU - Brueggenolte, Matthias
AU - Rinciog, Alexandru
AU - Henke, Michael
AU - Schweidtmann, Artur M.
AU - Gao, Qinghe
AU - Theisen, Maximilian F.
AU - El Shawi, Radwa
PY - 2024
Y1 - 2024
N2 - Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering. 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. The format is grounded in a systematic literature review and the didactic principles action orientation, constructivism, and problem orientation. At the heart of the concept lie ML challenges, which make use of industrial data sets to solve real-world problems. Micro-lectures enable students to learn about ML concepts and algorithms, and associated risks. They cover the entire ML pipeline, promoting data literacy and practical skills, from data preparation, through deployment, to evaluation.
AB - Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering. 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. The format is grounded in a systematic literature review and the didactic principles action orientation, constructivism, and problem orientation. At the heart of the concept lie ML challenges, which make use of industrial data sets to solve real-world problems. Micro-lectures enable students to learn about ML concepts and algorithms, and associated risks. They cover the entire ML pipeline, promoting data literacy and practical skills, from data preparation, through deployment, to evaluation.
KW - Machine learning
KW - education
KW - interdisciplinarity
KW - didactic concept
UR - http://www.scopus.com/inward/record.url?scp=85201794830&partnerID=8YFLogxK
U2 - 10.35011/IDIMT-2024-123
DO - 10.35011/IDIMT-2024-123
M3 - Conference contribution
AN - SCOPUS:85201794830
T3 - Schriftenreihe Informatik
SP - 123
EP - 131
BT - Proceedings IDIMT 2024 Changes to ICT, Management, and Business Processes through AI
A2 - Doucek, Petr
A2 - Sonntag, Michael
A2 - Nedomova, Lea
PB - TRAUNER Verlag + Buchservice GmbH
CY - Linz, Austria
Y2 - 4 September 2024 through 6 September 2024
ER -