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Reply To: | Classification, clustering, and phylogeny estimation |
Date: | Mon, 8 Jul 2002 00:28:14 -0700 |
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Dear colleague,
I thought that you or you co-workers might be interested in this course.
Please contact me if you have questions.
Sincerely
Professor Robert Tibshirani
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Short course: Statistical learning and data mining
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!
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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.
**********************************************
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
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