Kernel Methods

Kernel methods are popular approaches to fit scattered data. Applications range from computer graphics to data driven partial differential equations and machine learning. In this introductory lecture, the underlying theory and numerical methods are presented. The lecture is accompanied by implementation exercises to put the theory to practice.

This is an introduction to kernel methods providing basic concepts and algorithms.

In presence

Direct instruction plus accompanying exercises.

Written or oral exam depending on the number of participants