EduML: An explorative approach for students and lecturers in machine learning courses

Abstract

Due to its achievements in recent years, machine learning (ML) is now used in a wide variety of domains. Educating ML has hence become an important factor in academia and industry. We argue that students learning about machine learning will need, in addition to theoretical knowledge, approaches to interactively explore Machine Learning models and their parameters. This paper introduces EduML -an interactive approach for lecturers to teach and for students or professionals to study and explore the fundamentals of machine learning. EduML allows users to experiment with data preparation, dimensionality reduction and a wide range of classifiers on different data sets. These data sets can be analysed in order to understand the complexity of the classification problem. The classifiers can be autonomously fitted to the training data or the effect of manually altering model hyperparameters can be explored. Additionally, to get started with programming own ML pipelines, Python and R source code of configured ML pipelines can be extracted. EduML has been used in a lecture as an interactive demo or by students in lab sessions. Both scenarios were evaluated with a user survey.