This link has been bookmarked by 1 people . It was first bookmarked on 04 Feb 2010, by Mira Kwak.
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04 Feb 10
Mira Kwak"Short course: Statistical Learning and Data Mining III:
State-of-the-Art Statistical Methods for Data Analysis:
Ten Hot Ideas for Learning from Data
Trevor Hastie and Robert Tibshirani, Stanford University
Sheraton Palo Alto, California, March 18-19, 2010 "-
a detailed overview of statistical models for data mining, inference and prediction
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the third in a series, and follows our popular past offerings "Modern Regression and Classification", and "Statistical Learning and Data Mining"
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the tools useful for tackling modern-day data analysis problems. These include gradient boosting, SVMs and kernel methods, random forests, lasso and LARS, ridge regression and GAMs, supervised principal components, and cross-validation. We also present some interesting case studies in a variety of application areas.
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Topics include clustering and data visualization, false discovery rates and SAM, regularized logistic regression and discriminant analysis, supervised and unsupervised principal components, support vector machines and the kernel trick, and the careful use of model selection strategies.
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material
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Elements of Statistical Learning: data mining, inference and prediction
Hastie, Tibshirani & Friedman, Springer-Verlag, 2008 (2nd edition)A copy of this book will be given to all attendees.
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video-projected presentations and discussion
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