[R] Statistical Learning and Datamining Course October 2010 Washington DC

[Trevor Hastie]

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

 Trevor Hastie and Robert Tibshirani, Stanford University


 Georgetown University Conference Center
 Washington DC,
 October 11-12, 2010.

 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, Nonnegative Matrix Factorization
  * 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, 2009

 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.
 Go to the site

 http://www-stat.stanford.edu/~hastie/sldm.html

 for more information and online registration.