BCAM Course | Reproducing Kernel Hilbert Spaces for Machine Learning

This course introduces the mathematical tool of reproducing kernel Hilbert spaces (RKHSs) and describes multiple applications of such a tool in machine learning. In particular, we first use simple linear algebra to describe finite dimensional RKHSs, then we introduce the general construction and provide illustrative examples. The second part of the course describes multiple applications of RKHSs in machine learning including kernel-based supervised learning and kernel mean embedings. For researchers/students in machine learning, the course can make more accesible RKHSs as an important tool for machine learning that has been extensively used in the last decades. For researchers/students in other mathematical fields, the course can serve to introduce/recall a highly-relevant mathematical object and as a first approximation to the field of machine learning from a familiar standpoint.
 

Event website: https://www.bcamath.org/events/reproducing-kernel-Hilbert-spaces-for-machine-learning/en/