Material collected here comes primarily from Statistics courses 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):
- Typed (LaTEX) versions of course notes
- Slides for the material
- Videos for short talks/lectures on some 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:
A short expository article of Vardeman and Morris entitled “Modern Applied Supervised Statistical Learning from 30,000 Feet” putting much of modern predictive analytics into a unified framework is here:
Announcement: The 2nd Midwest Statistical Machine Learning Colloquium will be held in Ames, Iowa, Monday, May 13, 2019. For complete details, see the conference website here:
https://register.extension.iastate.edu/msmlc