MachineLearnAthon

Problem definition

Machine learning (ML), as a branch of artificial intelligence, is a tool for extracting information from data and developing models from it. With the help of these models, target values can be predicted, images or data points classified or groupings recognized. It enables process improvements and the efficient use of resources. There are areas of application in all specialist areas, but domain experts usually lack sufficient ML knowledge to achieve optimum results by combining their expertise with the algorithms.

Objective

The MachineLearnAthon is a European research project that develops teaching materials on ML for students from various disciplines. With its action-oriented teaching format, the project aims to improve data literacy and practical ML skills (from data acquisition to deployment). It aims to raise awareness of the potential and risks associated with ML. In addition, we promote international and interdisciplinary cooperation through the diversity of our consortium, consisting of chairs of computer science, business administration, chemical engineering and logistics.

Approach

Our teaching materials consist of micro-lectures, tutorials and use cases. They have a modular structure with the aim of addressing both students without any prior knowledge of ML and programming as well as students who already have experience in these areas. In the micro-lectures, videos with a length of approx. 10 minutes, we teach the basics and theoretical background of ML. In the tutorials, we show students how to implement the algorithms in the Python programming language. The use cases, consisting of real data sets that reflect an actual problem, serve to test and improve ML knowledge. They are also intended to motivate students to engage with the topic through their application relevance.