The EDRL research group works around a theoretical strain (embodied cognition), a methodological line (design-based research), and a disciplinary emphasis (mathematics). Thus, the laboratory hosts the full cycle of design-research projects that are geared to contribute to theory and practice of multi-modal mathematical learning and reasoning as well as to design theory.
Educational Studies in Mathematics presents new ideas and developments of major importance to practitioners working in the field of mathematical education. It reflects both the variety of research concerns within the field and the range of methods used to study them. Articles deal with didactical, methodological and pedagogical subjects, rather than with specific programs for teaching mathematics. The journal emphasizes high-level articles that go beyond local or national interest.
This course will give a detailed introduction to learning theory with a focus on the classification problem. It will be shown how to obtain (pobabilistic) bounds on the generalization error for certain types of algorithms. The main themes will be: * probabilistic inequalities and concentration inequalities * union bounds, chaining * measuring the size of a function class, Vapnik Chervonenkis dimension, shattering dimension and Rademacher averages * classification with real-valued functions Some knowledge of probability theory would be helpful but not required since the main tools will be introduced.