R help - Aug 2009 - Austria, September, 2009: Statistical Learning and Data Mining Course

Short course: Statistical Learning and Data Mining III:
Ten Hot Ideas for Learning from Data

Trevor Hastie and Robert Tibshirani, Stanford University

Danube University
Krems, Austria
25-26 September 2009

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, financial risk modeling, 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.

In this course we emphasize the tools useful for tackling modern-day
data analysis problems. From the vast array of tools available, we have
selected what we consider are the most relevant and exciting. Our
top-ten list of topics are:

* Regression and Logistic Regression (two golden oldies),
* Lasso and Related Methods,
* Support Vector and Kernel Methodology,
* Principal Components (SVD) and Variations: sparse SVD, supervised PCA,
   Multidimensional Scaling and Isomap, Nonnegative Matrix
    Factorization, and  Local Linear Embedding,
* Boosting, Random Forests and Ensemble Methods,
* Rule based methods (PRIM),
* Graphical Models,
* Cross-Validation,
* Bootstrap,
* Feature Selection, False Discovery Rates and Permutation Tests.

The material is based on recent papers by ourselves and other
researchers, as well as the new second edition of our book:

Elements of Statistical Learning: data mining, inference and prediction

Hastie, Tibshirani & Friedman, Springer-Verlag, 2009 (second edition)

http://www-stat.stanford.edu/ElemStatLearn/

A copy of this book will be given to all attendees.
The lectures will consist of video-projected presentations and
discussion.

Visit
http://www-stat.stanford.edu/~hastie/SLDM/Austria.htm
for more information and registration instructions.


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   Trevor Hastie                                   hastie at stanford.edu
   Professor & Chair, 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