Kernel Methods in Learning Theory (Course)

  • Lecturer: Nihat Ay
  • Date: Thursday 11:15 - 12:45, first meeting on November 19.
  • Room: Videobroadcast
  • Prerequisites: Linear algebra, elementary probability theory and functional analysis (the relevant results for this course will be summarised).
  • Remarks: Please register with Nihat Ay by November 15.

Abstract

This course will introduce into the theory of kernels and associated Hilbert spaces (reproducing kernel Hilbert spaces, RKHS), which play an important role within mathematical learning theory. In Statistical Learning Theory and the theory of Support Vector Machines (SVM), they provide efficient ways to formalise and control the generalisation ability of learning systems, based on the structural risk minimisation principle. A closely related inductive principle comes from regularisation theory. Here, learning is interpreted as an ill-posed inverse problem, where kernels define appropriate regularisers of the problem.

Regular lectures: Winter semester 2020/2021

24.11.2020, 02:30