Lectures
1. Modern Trends in Data Mining
2. Regularization Paths
Prof. Trevor Hastie
Professor of Statistics and Biostatistics
Stanford University, USA
Date: Wednesday, 9 April 2008
Time: 10.00h ? 12.30h
Venue: Fundación BBVA
Palacio del Marqués de Salamanca
Paseo de Recoletos, 10
28001 Madrid
PROGRAM
10.00-11.00 Modern Trends in Data Mining
As their ability to capture and organize large amounts of data
increases, organizations rely more on data mining technology to learn
from this valuable resource. We will give several examples of this
process, based on our own experiences. This talk will give a
brief overview of some of the most promising new methods for
"supervised" learning, including the lasso, random forests, boosting,
and SVMs (support vector machines).
11.30-12.30 Regularization Paths
Regularization is a popular approach to model selection, with L2 and L1
taking center stage. Recently there has been a spate of research on
efficient algorithms for computing regularization paths. In this talk
I will touch on a number of areas:
-- Boosting and its forward-stagewise regularization path
-- Lars and glmpath software for R, and other programs.
-- Degrees of freedom and inference along paths
-- Path- and coordinate-wise approaches
-- SVMs and the role of regularization
PROF. TREVOR HASTIE
Trevor Hastie, formerly at AT&T Bell Laboratories, joined the Department of Statistics at Stanford University in 1994. His main research contributions have been in the field of applied nonparametric regression and classification, and he has written two
books in this area: "Generalized Additive Models" (with R. Tibshirani, Chapman and Hall, 1991), and "Elements of Statistical Learning" (with R. Tibshirani and J. Friedman, Springer 2001). He has also made contributions in statistical computing, co-editing (with J. Chambers) a large software library on modeling tools in the S language ("Statistical Models in S", Wadsworth, 1992), which form the basis for much of the statistical modeling in R and S-plus. His current research focuses on applied problems in biology and genomics, medicine and industry, in particular data mining, prediction and classification problems. He is visiting Madrid as the guest of the Fundación BBVA, where he is presenting a two-day course on Statistical Learning in the Foundation's program of statistical workshops, organized by Michael Greenacre.
We are pleased to invite you to attend these lectures, and would also request that you pass on the details to anyone else you believe may be interested.
Thanks in advance for your attention,
Information: (+34) 91 374 68 57 / 91 374 85 01
www.fbbva.es
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Please confirm attendance ? entrance free; limited seating
Simultaneous translation will be provided
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Fundación BBVA
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