SLDM Statistical Learning and Data Mining III – 10 Hot Ideas, Palo Alto, Mar 20-21
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.
Robert Tibshirani and Trevor Hastie, Stanford University
Sheraton Hotel, Palo Alto, California - March 20-21, 2014
This two-day coursegives 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,
- Feature Selection, False Discovery Rates and Permutation Tests.
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
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
www-stat.stanford.edu/~hastie/sldm.html for more information and online registration.
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