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(The following description was used when this course was taught during the Spring 2007 Semester. There will likely be differences in the instructor, textbook, and/or outline the next time the course is taught.)
Instructor: Dr. Yu Ding
Textbook: Hastie, T., Tibshirani, R., Friedman, J., 2001, The Elements of Statistical Learning: Data-mining, Inference, and Prediction , Springer-Verlag , New York , NY.
Reference on machine learning: Cherkassky, V. and Mulier, F., 1998, Learning From Data: Concepts, Theory, and Methods , John Wiley and Sons, New York .
References on spatial and temporal modeling :
Santner, T. J., Williams, B.J., Notz, W.I., 2003, Design and Analysis of Computer Experiments, Springer Series in Statistics, Springer Verlag
S. M. Pandit and S.M. Wu, 2001, Time Series and System Analysis with Applications , Krieger Editions of 1983, 1990, 1993 are almost the same .
References on Linear/Matrix Algebra :
Strang, G., 1988, Linear Algebra and Its Applications , 3 rd edition, Harcourt Brace Jovanovich.
Schott, J.R., 1997, Matrix Analysis for Statistics , John Wiley and Sons.
Description: Fundamentals related to empirical model building for engineering applications, assessment of empirical model building, model complexity, bias-variance decomposition, model selection criteria, computational procedures, various types of predictive models.
Course Topics
- A simple predictive model: linear regression
- Subset selection and shrinkage
- Model complexity and assessment
- Model selection criteria (AIC, BIC, V-C dimension)
- Cross-validation and bootstrapping
- Spatial predictive model (Kriging)
- Temporal predictive model (Time Series) Other predictive models (Tree, MART, SVM)
