The Course at a Glance
We introduce and motivate the main theme of the course, setting the
problem of learning from examples as the problem of approximating a
multivariate function from sparse data. We present an overview of the
theoretical part of the course and sketch the connection between
classical Regularization Theory and its algorithms -- including
Support Vector Machines -- and Learning Theory, the two cornerstones
of the course. We mention theoretical developments during the last few
months that provide a new perspective on the foundations of the
theory. We briefly describe several different applications ranging
from vision to computer graphics, to finance and neuroscience.
Slides for this lecture: PDF.