Abstract is missing.
- Developing Open Source Educational Resources for Machine Learning and Data ScienceLudwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl. 1-6 [doi]
- Introduction to AI and its medical applications: Crash Course for an audience with diverse scientific backgroundsDonatella Cea, Helene Hoffmann, Marie Piraud. 7-11 [doi]
- Teaching Machine Learning with Applied Interdisciplinary Real World ProjectsGulustan Dogan. 12-15 [doi]
- Hearts Gym: Learning Reinforcement Learning as a Team EventJan Ebert, Danimir T. Doncevic, Ramona Kloß, Stefan Kesselheim. 16-21 [doi]
- Stimulating student engagement with an AI board game tournamentKen Hasselmann, Quentin Lurkin. 22-26 [doi]
- Will the sun shine? - An accessible dataset for teaching machine learning and deep learningFlorian Huber, Erica Dafne van Kuppevelt, Peter Steinbach, Colin Sauze, Yang Liu, Berend Weel. 27-31 [doi]
- A Deep Learning Bootcamp for Engineering & Management StudentsLukas Lodes, Alexander Schiendorfer. 32-36 [doi]
- Data, Trees, and Forests - Decision Tree Learning in K-12 EducationTilman Michaeli, Stefan Seegerer, Lennard Kerber, Ralf Romeike. 37-41 [doi]
- Teaching Machine Learning with mlr3 using ShinyGero Szepannek, Laurens Martin Tetzlaff, Alexander Frahm, Karsten Lübke. 42-45 [doi]
- Machine Learning Students Overfit to OverfittingMatias Valdenegro-Toro, Matthia Sabatelli. 46-51 [doi]