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!
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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/ElemStatLearn/
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.
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