This set of notes is the most recent reorganization and update-in- progress of Modern Multivariate Statistical Learning course material de- veloped 2009-2020 over 7 offerings of PhD-level courses and 4 offerings of an MS-level course in the Iowa State University Statistics Department, a short course given in the Statistics Group at Los Alamos National Lab, and two offered through Statistical Horizons LLC. Early versions of the courses were based mostly on the topics and organization of The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman, though very substantial parts benefited from Izenman's Modern Multivariate Statis- tical Techniques, and from Principles and Theory for Data Mining and Machine Learning by Clarke, Fokoue, and Zhang.

The present version benefits from a thougtful set of written comments on an earlier iteration of the notes provided by Ken Ryan and Mark Culp, incisive observations on the material and suggestions concerning what I've said about it made by Max Morris and Huaiqing Wu during the MS- level course we taught together Spring 2014, additional helpful critques offered by LANL statisticians in Summer 2016, and material from Bishop's Pattern Recognition and Machine Learning, Applied Predictive Modeling by Kuhn and Johnson, and An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani. The work of a number of ISU PhD and MS advisees inlcuding Jing Li, Wen Zhou, Cory Lanker, Andee Kaplan, and Abhishek Chakraborty has also provided useful additional content reflected in this version.

These notes have as prerequisites the Statistical Theory, Methods, and Computing content of the first year courses in a Statistics MS program, though presumably much of them can be understood with less background.