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.
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