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.

翻译：机器学习（ML）技术如今已跨越学科边界，从社会科学、自然科学到工程领域均有应用。ML的广泛应用及其加速演进的趋势，催生出对专门教学理念的迫切需求，旨在使该技术的应用更加可靠和负责。然而，ML教学是一项艰巨的任务。除却ML算法在理论与实现层面的方法复杂性，还需考量该学科的跨学科与实证特性。本文提出MachineLearnAthon这一创新教学理念，专为不同学科背景、数学/编程与领域专业知识水平各异的学生设计。该理念的核心是ML挑战任务——利用工业数据集解决实际问题，任务涵盖从数据准备、部署到评估的完整ML流程，旨在提升数据素养与实践技能。