This course focuses on "interactive machine learning" (iML). We define iML as machine learning in which the human being is a central part, interactively intervening at various points in the learning process. He monitors the results of the machine and provides input and corrections to improve the learning process. While traditional ML systems limit human input to the provision of annotations, iML is concerned with creating interaction design guidelines for ML systems, and developing new methods to incorporate human expertise into the ML process. This makes systems more transparent and understandable, which overlaps with the current emerging research trend of "explainable AI" (XAI). The course consists of several practical hands-on projects and an introduction to various topics related to "interactive machine learning".
