The research group Machine Learning (HammerLab) was established at Bielefeld University on April 1st, 2010, as part of the CITEC center of excellence and the Faculty of Technology. The research of the Machine Learning group centers around key enabling technologies for machine learning, its theoretical foundations and applications in diverse areas such as biomedical data analysis, industry 4.0 and resilience of critical infrastructure. Typical data analysis tasks include data clustering, data visualization, the inference of data models for classification, regression, or density estimation, relevance learning and feature extraction, etc. Due to improved sensor technologies, dedicated data formats and storage facilities, these classical objectives face severe challenges which make the development of novel data analysis technology necessary: data are becoming extremely heterogeneous and high dimensional, often, multiple modes and additional structural information are available. In addition, extremely large data sets have to be dealt with. These challenges can be faced using technologies such as XAI, metric learning, hybrid symbolic-subsymbolic systems, and various approximations for streaming data.

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