Michael Steele has this course on “Probability Inequalities and Machine Learning”. This is the kind of course I’ve really liked to take. Anyway, for most of us who cannot attend the course, following his suggested reading may help somewhat.
There are some known papers/lecture notes in the reading list; such as Gabor Lugosi’s “Concentration of measure inequality” which is very readable.
Also in the page I found this paper by Mease and Wyner entitled “Evidence Contrary to the Statistical View of Boosting”. I definitely should read it.
well … that’s it for now!