10302 Summary – Learning paradigms in dynamic environments

Abstract

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.