[BiO BB] data mining short course

Hai Zhang seasea at hotmail.com
Wed Aug 25 23:43:19 EDT 2004


Dear Dr. Tibshirani,

I really love to attend this course.  Because I am not sure about the status
of my visa to United States.  Would you have other suggestions for the
application?

Thanks a lot.

With best regards,
Hai

----- Original Message -----
From: "Robert J. Tibshirani" <tibs at stat.Stanford.EDU>
To: <bio_bulletin_board at bioinformatics.org>
Sent: Thursday, August 26, 2004 1:10 AM
Subject: [BiO BB] data mining short course


>
> Short course: Statistical Learning and Data Mining
>
> Trevor Hastie and Robert Tibshirani, Stanford University
>
> Georgetown University Conference Center
> Washington DC
> September 20-21, 2004
>
> 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 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 sequel to our popular "Modern Regression and Classification"
> course covers many new areas of unsupervised learning and data mining,
> and gives an in-depth treatment of some of the hottest tools in
> supervised learning.
>
> The first course is not a prerequisite for this new course.
> Most of the techniques discussed in the course are implemented by the
> authors and others in the S language (S-plus or R), and all of the
> examples were developed in S.
>
> Day one focuses on state-of-art  methods for supervised
> learning, including PRIM, boosting, support vector machines,
> and very recent work on least angle regression and the lasso.
>
> Day two covers unsupervised learning, including clustering, principal
> components, principal curves and self-organizing maps.  Many
> applications will be discussed, including the analysis of DNA
> expression arrays - one of the hottest new areas in biology!
>
> ###################################################
> Much of the material is based on the book:
>
> Elements of Statistical Learning: data mining, inference and prediction
>
> Hastie, Tibshirani & Friedman, Springer-Verlag, 2001
>
> http://www-stat.stanford.edu/ElemStatLearn/
>
> A copy of this book will be given to all attendees.
>
> ###################################################
>
> For more information, and to register, visit the course homepage:
>
> http://www-stat.stanford.edu/~hastie/mrc.html
>
> _______________________________________________
> BiO_Bulletin_Board maillist  -  BiO_Bulletin_Board at bioinformatics.org
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>



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