Short course: Statistical Learning and Data Mining III: Ten Hot Ideas for Learning from Data
Trevor Hastie and Robert Tibshirani, Stanford University Danube University Krems, Austria 25-26 September 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. The material is based on recent papers by ourselves and other researchers, as well as the new second edition of our book: Elements of Statistical Learning: data mining, inference and prediction Hastie, Tibshirani & Friedman, Springer-Verlag, 2009 (second edition) http://www-stat.stanford.edu/ElemStatLearn/ A copy of this book will be given to all attendees. The lectures will consist of video-projected presentations and discussion. Visit http://www-stat.stanford.edu/~hastie/SLDM/Austria.htm for more information and registration instructions. ------------------------------------------------------------------- Trevor Hastie has...@stanford.edu Professor & Chair, Department of Statistics, Stanford University Phone: (650) 725-2231 (Statistics) Fax: (650) 725-8977 (650) 498-5233 (Biostatistics) Fax: (650) 725-6951 URL: http://www-stat.stanford.edu/~hastie address: room 104, Department of Statistics, Sequoia Hall 390 Serra Mall, Stanford University, CA 94305-4065 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
