Rob Tibshirani, Stanford University

Hugh Chipman, Acadia University
 

One-Day Short Course, 25 May 2014, 9:00am - 4:30pm (lunch break 12:00 - 1:30) room MS 2172, Medical Sciences building, University of Toronto
 

This workshop gives an overview of statistical models for data mining, inference and prediction. With the increasing availability of "Big Data", statistical skills such as visualization, statistical learning and modelling are in great demand.  The course provides an in-depth treatment of some of the main tools in supervised learning, including the lasso and sparsity-based methods, random forests, and boosting.  It also covers many new areas of unsupervised learning and data mining including visualization, linear and nonlinear dimension reduction, clustering, and analysis of complex data structures such as functional data and network data.  Computation, including R implementations of many methods, will be discussed.  Real examples from business, industry and other areas will be used to demonstrate ideas throughout the workshop.