Statistical learning and data mining

Trevor Hastie and Robert Tibshirani, Stanford Univ.
Cambridge, Mass. Sep 6-7, 2001


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!

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Much of the material is based on the upcoming book:

  Elements of Statistical Learning: data mining, inference and prediction

(with J. Friedman, Springer -Verlag, 2001).
 A copy of this book will be given to all attendees.

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The first offering of this new course will take place Sep 6-7, 2001,
in Cambridge, Mass.

Further details are available at

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


Please Email me if you have specific questions
([log in to unmask]).



**********************************************
Rob Tibshirani, Dept of Health Research & Policy
 and Dept of Statistics
HRP Redwood Bldg
Stanford University
Stanford, California 94305-5405

phone: HRP: 650-723-7264 (Voice mail),  Statistics 650-723-1185
FAX 650-725-8977
[log in to unmask]
http://www-stat.stanford.edu/~tibs