[R] Statistical Learning and Data Mining Course

From: Trevor Hastie <hastie_at_stanford.edu>
Date: Tue 13 Jul 2004 - 05:25:53 EST


Short course: Statistical Learning and Data Mining  

Trevor Hastie and Robert Tibshirani, Stanford University  

Georgetown University Conference Center
Washington DC
September 20-21, 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. Most of the techniques discussed in the course are implemented by the authors and others in the S language (S-plus or R), and all of the examples were developed in S.  

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 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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For more information, and to register, visit the course homepage:  

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


  Trevor Hastie                                  hastie@stanford.edu  
  Professor, Department of Statistics, Stanford University
  Phone: (650) 725-2231 (Statistics)          Fax: (650) 725-8977  
  (650) 498-5233 (Biostatistics) Fax: (650) 725-6951   URL: http://www-stat.stanford.edu/~hastie   address: room 104, Department of Statistics, Sequoia Hall

           390 Serra Mall, Stanford University, CA 94305-4065


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