Courses

Trevor Hastie and Robert Tibshirani, Stanford Univ.

Georgetown University Conference Center Washington, D.C Sep. 19-20, 2002

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 pre-requisite for this new course.

Day one focusses on state-of-art methods for supervised learning including PRIM, boosting and support vector machines. Day two covers unsupervised learning including clustering, principal components, principal curves and self-organizing maps. Many applications will be discussed, including DNA expression arrays. These are 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/~tibs/ElemStatLearn/index.html

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

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go to the site

http://www-stat.stanford.edu/~hastie/mrc.html

for more information and online registration.