Statistical Learning and Data Mining III, Boston, Oct 2728
Taught by top Stanford professors and leading statisticians Trevor Hastie and Robert Tibshirani, this course presents 10 hot ideas for learning from data, and gives a detailed overview of statistical models for data mining, inference and prediction.
StateoftheArt Statistical Methods for Data Analysis:
Ten Hot Ideas for Learning from Data
Trevor Hastie and Robert Tibshirani, Stanford University
Harvard Conference Center, Boston, MA  October 2728, 2014
This twoday 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 hightech 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 modernday data analysis problems. From the vast array of tools available, we have selected what we consider are the most relevant and exciting.
Our topten list of topics are:
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, SpringerVerlag, 2008.
A copy of this book will be given to all attendees.
The lectures will consist of videoprojected presentations and discussion. Go to the site
wwwstat.stanford.edu/~hastie/sldm.html for more information and online registration.
Ten Hot Ideas for Learning from Data
Trevor Hastie and Robert Tibshirani, Stanford University
Harvard Conference Center, Boston, MA  October 2728, 2014
This twoday 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 hightech 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 modernday data analysis problems. From the vast array of tools available, we have selected what we consider are the most relevant and exciting.
Our topten 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,
 CrossValidation,
 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, SpringerVerlag, 2008.
A copy of this book will be given to all attendees.
The lectures will consist of videoprojected presentations and discussion. Go to the site
wwwstat.stanford.edu/~hastie/sldm.html for more information and online registration.
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