We wanted to implement an innovative, interdisciplinary teaching format that enables students from diverse academic backgrounds to acquire fundamental ML competencies. We aimed to empower them to recognize ML potentials within their fields and assess possible risks and limitations. To achieve this, we developed a didactic concept grounded in action orientation, constructivism, and problem orientation. This included micro lectures—short learning videos that convey essential foundations of ML—and complementary tutorials that demonstrate practical implementation. These materials were combined with hands‑on challenges of varying difficulty levels based on real‑world problems. Working on authentic use cases motivated students by allowing them to experience how ML can be applied in realistic contexts. In addition, the project sought to strengthen students’ digital readiness and enhance their data literacy, enabling them to handle data responsibly. Moreover, we aimed to integrate the MachineLearnAthon into existing curricula across partner universities to ensure sustainable impact. Ultimately, the initiative fosters interdisciplinary teamwork and prepares students for responsible participation in data‑driven careers.
We applied for this project in response to growing interest from students who increasingly seek opportunities to learn Machine Learning (ML). Surveys show that they value flexible online formats, social interaction, and hands‑on projects with real‑world relevance. Career prospects also play a decisive role—students recognize that ML skills are essential for their future in data‑driven industries. However, while ML is well integrated into Computer Science programs, it remains underrepresented in Engineering, Logistics, Business Management, and Economics—fields where learners see its importance but lack access to structured training. At the same time, industry demands qualified professionals able to apply ML responsibly within their domains. As Artificial Intelligence is an internationally relevant topic shaping all sectors, we considered it particularly suitable for a European project that combines perspectives and expertise from different countries. MachineLearnAthon addresses these needs through flexible online micro‑lectures and collaborative work on authentic use cases provided by companies.