Subject:
[R] new data mining course
From:
Trevor Hastie <hastie@stanford.edu>
Date:
Fri, 19 Aug 2005 12:29:49 -0700
To:
r-help@stat.math.ethz.ch

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

Trevor Hastie and Robert Tibshirani, Stanford University

The Conference Center at Harvard Medical School
Boston, MA,
Oct 31-Nov 1, 2005

This is a *new*  two-day course on statistical models
for data mining, inference and prediction. It is the third
in a series, and follows our past
offerings "Modern Regression and Classification", and "Statistical
Learning and Data Mining".

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 publically available
R packages.

Please visit the site
http://www-stat.stanford.edu/~hastie/sldm.html
for more information on the course 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 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html