[R] New Statistical Learning and Data Mining Course

[Trevor Hastie]

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

  Trevor Hastie and Robert Tibshirani, Stanford University

  Sheraton Hotel
  Palo Alto, CA
  March 16-17, 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.

  Our earlier courses are not a prerequisite for this new course.  
Although
  there is some overlap with past courses, our new course contains many
  topics not covered by us before.

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

Statistical Learning: data mining, inference and prediction

Hastie, Tibshirani & Friedman, Springer-Verlag, 2008

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

  A copy of this book will be given to all attendees.
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  The lectures will consist of video-projected presentations and
  discussion.
  Go to the site
  http://www-stat.stanford.edu/~hastie/sldm.html
  for more information and online registration.