Data Mining Course

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Data Mining Course

Trevor Hastie
Short course: Statistical Learning and Data Mining II:
                 tools for tall and wide data

Trevor Hastie and Robert Tibshirani, Stanford University

Sheraton Hotel,
Palo Alto, California,
April 3-4, 2006.

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  

This course is the third in a series, and follows our popular past
offerings "Modern Regression and Classification", and "Statistical
Learning and Data Mining".

The two earlier courses are not a prerequisite for this new course.

In this course we emphasize the tools useful for tackling modern-day
data analysis problems. We focus on both "tall" data ( N>p where
N=#cases, p=#features) and "wide" data (p>N). The tools include
gradient boosting, SVMs and kernel methods, random forests, lasso and
LARS, ridge regression and GAMs, supervised principal components, and
cross-validation.  We also present some interesting case studies in a
variety of application areas. All our examples are developed using the
S language, and most of the procedures we discuss are implemented in
publicly available R packages.

Please visit the site
for more information and registration details.

   Trevor Hastie                                   [hidden email]
   Professor, Department of Statistics, Stanford University
   Phone: (650) 725-2231 (Statistics)          Fax: (650) 725-8977
   (650) 498-5233 (Biostatistics)   Fax: (650) 725-6951
    address: room 104, Department of Statistics, Sequoia Hall
            390 Serra Mall, Stanford University, CA 94305-4065

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