This course has been given in November 2008 at the Computational Intelligence and Learning doctoral school. It gives a rather complete and self contained introduction to statistical learning theory for students and researchers with a good background in probability and statistics, and with some familiarity with machine learning.
Slides are available here.
This course has been mostly prepared with the help of the excellent book by Luc Devroye, László Györfi et Gábor Lugosi, A Probabilistic Theory of Pattern Recognition, published by Springer in 1996. Despite its age, I believe this book to be a perfect reference on the subject because of its extraordinary writing quality.
I've also used many articles to prepare the course; most of them are freely available online: