ECE 901: Statistical Learning Theory

Prerequisites: 
Background in applied mathematics, probability, and statistics
Instructor: 

        Robert Nowak
        E-mail: nowak@engr.wisc.edu
        Web: http://www.ece.wisc.edu/~nowak/
        Phone:  608 265 3914 
        3627 Engineering Hall
        Office Hours: TBA

Lectures: 
        Spring 2007
        Time/Place: 11:00-12:15pm Tuesday and Thursday / 2341 Engineering Hall

Course Format: 
The course will consist of 15-20 introductory lectures,  
followed by readings and discussion of recent developments

Lectures: 
Lecture 0 (pdf) Statistical Decision and Learning Theory

Homework Problems: TBA

Readings: TBA


Textbooks and References:

A textbook will not be followed in this course.  A collection of
notes, relevant papers and materials will be prepared and distributed.
Textbooks recommended for further reading are listed below.

A probabilistic theory of pattern recognition, Devroye, Gyorfi, Lugosi, Springer
Nonparameteric Estimation Theory, Iain Johnstone, unpublished monograph
The Elements of Statistical Learning, Hastie, et al, Springer
An introduction to support vector machines, Cristianini and Shawe-Taylor, Cambridge Press
Combinatorial methods in density estimation, Devroye and Lugosi, Springer
Statistical Learning Theory, Vapnik, Wiley
An Introduction to Computational Learning Theory, Kearns and Vazirani, MIT Press
Empirical Processes in M-Estimation, van de Geer, Cambridge Press
Grading and Evaluation:

Grades will be based on course participation, projects, and paper presentations.