Vortragender ist Jun.-Prof. Dr- Michael Sedlmair von der Universität Stuttgart.
Over the last years, the field of machine learning has substantially changed the work in many different scientific disciplines, including the visualization community. Based on our experience of conducting projects at the intersection of machine learning (ML) and interactive visualization (Vis) over the last decade, my talk will reflect on and discuss the current relation between these two areas. For that purpose, the talk’s structure will follow two main ideas. First, I will talk about *Vis for ML*, that is, the idea that visualization can help machine learning researchers and practitioners gaining interesting insights into the models they are building. Here, I will specifically focus on visual parameter space analysis, and illustrate how this approach can help to better understand ML models, such as dimensionality reduction, clustering, and classification models. In the second part, I will turn the relationship around and discuss the contribution that *ML for Vis* can make. While other communities seem to have been much quicker in adopting ML pipelines, ML for Vis has gained little attention yet, but bears the potential to automatize the visualization design process. This new approach might potentially lead to a fundamental paradigm shift in how visualization research and design will be done in the future.