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Unformatted text preview: Sta 414/2104 S http://www.utstat.utoronto.ca/reid/414S10.html STA414S/2104S: Statistical Methods for Data Mining and Machine Learning January  April, 2010 , Tuesday 122, Thursday 121, SS 2105 Course Information This course will consider topics in statistics that have played a role in the development of techniques for data mining and machine learning. We will cover linear methods for regression and classification, nonparametric regression and classification methods, generalized additive models, aspects of model inference and model selection, model averaging and tree based methods. Prerequisite : Either STA 302H (regression) or CSC 411H (machine learning). CSC108H was recently added: this is not urgent but you must be willing to use a statistical computing environment such as R or Matlab. Office Hours : Tuesdays, 3‐4; Thursdays, 2‐3; or by appointment. Textbook : Hastie, Tibshirani and Friedman. The Elements of Statistical Learning . Springer‐Verlag. http://www‐stat.stanford.edu/~tibs/ElemStatLearn/index.html Course evaluation : • Homework 1 due February 11: 20%, • Homework 2 due March 4: 20%, • Midterm exam, March 16: 20%, • Final project due April 16: 40%. 1 / 20 Tentative Syllabus I Regression : linear, ridge, lasso, logistic, polynomial splines, smoothing splines, kernel methods, additive models, regression trees, projection pursuit, neural networks: Chapters 3, 5, 9, 11 I Classification : logistic regression, linear discriminant analysis, generalized additive models, kernel methods, naive Bayes, classification trees, support vector machines, neural networks, Kmeans, knearest neighbours, random forests: Chapters 4, 6, 9, 11, 12, 15 I Model Selection and Averaging : AIC, crossvalidation, test error, training error, bootstrap aggregation: Chapter 7, 8.7 I Unsupervised learning : Kmeans clustering, knearest neighbours, hierarchical clustering: Chapter 14 2 / 20 Some references I Venables, W.N. and Ripley, B.D. (2002). Modern Applied Statistics with S (4th Ed.) . SpringerVerlag. Detailed reference for computing with R . I Maindonald, J. and Braun, J. (). Data Analysis and Graphics using R . Cambridge University Press. Gentler source for R information. I Hand, D., Mannila, H. and Smyth, P. (2001). Principals of Data Mining . MIT Press. Nice blend of computer science and statistical methods. I Ripley, B.D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press. Excellent, but concise, discussion of many machine learning methods. 3 / 20 Some Courses I http://www.stat.columbia.edu/ ˜ madigan/DM08 Columbia (with links to other courses) I http://wwwstat.stanford.edu/ ˜ tibs/stat315a.html Stanford I http://www.stat.cmu.edu/ ˜ larry/=sml2008/ CarnegieMellon 4 / 20 Data mining/machine learning I large data sets I high dimensional spaces I potentially little information on structure I computationally intensive I plots are essential, but require considerable preprocessing I emphasis on means and variances I emphasis on prediction...
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This document was uploaded on 08/12/2010.
 Spring '09
 Statistics

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