Learning paradigms in dynamic environments

The seminar centered around problems which arise in the context of machine learning in dynamic environments. Particular emphasis was put on a couple of specific questions in this context: how to represent and abstract knowledge appropriately to shape the problem of learning in a partially unknown and complex environment and how to combine statistical inference and abstract symbolic representations; how to infer from few data and how to deal with non i.i.d. data, model revision and life-long learning; how to come up with efficient strategies to control realistic environments for which exploration is costly, the dimensionality is high and data are sparse; how to deal with very large settings; and how to apply these models in challenging application areas such as robotics, computer vision, or the web. 1 Goals of the seminar Machine learning techniques such as neural networks and statistical counterparts constitute particularly successful tools with numerous applications ranging from industrial tasks up to the investigation and simulation of biological systems. Unlike their biological paragon, however, they are often restricted to very narrow settings such as dedicated classification and regression tasks and simple data structures such as real vectors, neglecting the rich repertoire of biological networks in complex dynamic behavior, hierarchical organization, and implicit evolution of structures. In particular, classical learning models such as neural networks, decision trees, or vector quantizers are often restricted to purely feedforward settings and simple vectorial data instead of dynamic environments which include rich structure. Further, the learning scenario which is usually considered in technological settings is by far too restricted not only concerning recurrence, but concerning