Olivier Bousquet1, Stephane Boucheron2, and Gabor Lugosi3 1 Max-Planck Institute for Biological Cybernetics Spemannstr. 38, D-72076 Tubingen, Germany olivier.bousquet@m4x.org WWW home page: http://www.kyb.mpg.de/~bousqu... 2 Universite de Paris-Sud, Laboratoire d'Informatique B^atiment 490, F-91405 Orsay Cedex, France stephane.boucheron@lri.fr WWW home page: http://www.lri.fr/~bouchero 3 Department of Economics, Pompeu Fabra University Ramon Trias Fargas 25-27, 08005 Barcelona, Spain lugosi@upf.es WWW home page: http://www.econ.upf.es/~lugosi Abstract. The goal of statistical learning theory is to study, in a statistical framework, the properties of learning algorithms. In particular, most results take the form of so-called error bounds. This tutorial introduces the techniques that are used to obtain such results. - Paul Delhanty