Download Statistics 8932 - Statistical Learning and Data Mining

Statistics 8932 - Statistical Learning and Data Mining
Spring 2009
Instructor: SHEN, X., Fort Hall 381, Ph: 624-7098, Email: [email protected].
Office hours: TuTh 11:00pm–12:00pm, or by appointment.
Text: Elements of Statistical Learning, Data Mining, Inference, and Prediction, by Hastie, T.,
Tibshirani, R., and Friedman, J., 2002, 2rd ed. Springer.
Lectures: TuTh 10:10pm–11:00pm, FordH 115.
Grades: Grading will be based on homework assignments and presentations.
Reference:
The Nature of Statistical Learning Theory, by Vapnik, V., 1995. Springer.
An Introduction to Support Vector Machines, by Cristianini, N., and Shawe-Taylor, J., 2000. Cambridge.
Course Descriptions
This course introduces various modern statistical techniques for information extraction and processing, in particular regression, supervised learning and semisupervised learning, and model assessment,
model selection and combination. Topics to be covered include linear discriminant analysis, large margin classification such as support vector machines, model combination such as ensemble methods, among
others. The focus will be on current research developments.
Tentative Course outlines:
1. Introduction. (1 week).
2. Linear methods, smoothing, the method of regularization for regression and classification (2 weeks).
3. Model assessment and selection. (1-2 weeks).
4. Supervised learning and large margin classification (3 weeks).
5. Unsupervised and semisupervised learning (2-3 weeks).
6. Structured learning. (1-2 weeks)
7. Student Presentation (1-2 week).