Courses

Trevor Hastie and Robert Tibshirani, Stanford University

Sheraton Hotel Palo Alto, CA Feb 26-27, 2004

This two-day course gives a detailed overview of statistical models for data mining, inference and prediction. With the rapid developments in internet technology, genomics and other high-tech industries, we rely increasingly more on data analysis and statistical models to exploit the vast amounts of data at our fingertips.

This sequel to our popular "Modern Regression and Classification" course covers many new areas of unsupervised learning and data mining, and gives an in-depth treatment of some of the hottest tools in supervised learning.

The first course is not a prerequisite for this new course.

Day one focuses on state-of-art methods for supervised learning, including PRIM, boosting, support vector machines, and very recent work on least angle regression and the lasso.

Day two covers unsupervised learning, including clustering, principal components, principal curves and self-organizing maps. Many applications will be discussed, including the analysis of DNA expression arrays - one of the hottest new areas in biology!

Much of the material is based on the best selling book:

Elements of Statistical Learning: data mining, inference and prediction

Hastie, Tibshirani & Friedman, Springer-Verlag, 2001

http://www-stat.stanford.edu/ElemStatLearn/

A copy of this book will be given to all attendees.

Go to the site http://www-stat.stanford.edu/~hastie/mrc.html for more information and online registration.