Material collected here comes primarily from a PhD-level Statistics course originally developed to cover the topics of Hastie, Tibshirani, and Friedman’s *The Elements of Statistical Learning*. By now much is owed not only to that book, but to Izenman’s *Modern Multivariate Statistical Techniques*, *Principles and* *Theory for Data Mining and Machine Learning* by Clarke, Fokoue, and Zhang, Bishop’s *Pattern Recognition and Machine Learning*, as well as several other sources.

Available are (or will be):

- A typed (LaTEX) version of course notes
- A set of slides for the material
- Videos for short talks/lectures on course topics

A set of overview slides from a colloquium talk (given at Los Alamos National Lab, July 16, 2014) entitled “One Statistician’s Perspectives on Statistics and ‘Big Data’ Analytics: Some (Ultimately Unsurprising) Lessons Learned” can be found here: