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: