They’ve shaped a whole generation of data scientists in areas of machine learning and statistics, all the while delivering the material in a hilarious and engaging style — can’t thank you two enough! Stanford University Department of Statistics Department of Biomedical Data Science, Stanford School of Medicine Site Map Homepage Biography Research Software & Data Lectures and Talks Students Publications books A short course given by Trevor Hastie and Robert Tibshirani both of Stanford University This course is the fifth in a series, and follows our popular past offerings: Modern Regression and Classification (1996-2000) Statistical Learning and Data Mining (2001-2005) I found it to be an excellent course in statistical learning Statistical Learning and Data Mining Course Information on the two-day course This course is taught by Trevor Hastie and Rob Tibshirani. Course leader: Josephine Sullivan Email: sullivan@csc.kth.se Description of the course. In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). Originally from South Africa and Canada, today, they are both firmly seated at Stanford. Methods for improving the performance of weak learners. He received his university education from Rhodes University, South Africa (BS), University of Cape Town (MS), and Stanford University (Ph.D Statistics 1984). Trevor Hastie was born in South Africa in 1953. In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). This PhD level course will be given in English in the Spring of 2012. January 2003 Trevor Hastie, Stanford University 2 Outline • Model Averaging • Bagging • Boosting • History of Boosting • Stagewise Additive Modeling • Boosting and Logistic Regression • MART • Boosting and Overfitting • Summary of Boosting, and its place in the toolbox. All lists s tart with the phenomenal duo of Trevor Hastie and Robert Tibshirani. This graduate course is based on the book Elements of Statistical Learning (second edition) by Trevor Hastie, Robert Tibshirani and Jerome Friedman, 2009. A comprehensive introduction to key statistical learning concepts, models, and ideas by Robert Tibshirani, Trevor Hastie, and Daniela Witten. He is a Professor in Statistics and statistics in Stanford University.

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