[R] Data Mining Course

From: Trevor Hastie <hastie_at_stanford.edu>
Date: Sun 15 Jan 2006 - 08:11:51 EST

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 fingertips.

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
http://www-stat.stanford.edu/~hastie/sldm.html for more information and registration details.

   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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