EE511 -- Introduction to Statistical Learning

Course Information, Spring 2008


Course Hours: T,Th 2:30-4:20
Location: EEB 025

Mari Ostendorf (mo@ee)
Office: EE Rm 215D (inside 215, door by 205)
Office Hours: Weds 3:30-5:00
Kevin Duh (kevinduh@u)
Office Hours: Tu 4:30-5:30 (EEB M306)

Grader: Anna Margolis (amargoli@u)

Course description:
The course covers classification and estimation of vector observations, including both parametric and nonparametric approaches. Topics include classification with likelihood functions and general discriminant functions; density estimation; supervised, semi-supervised and unsupervised learning; feature reduction and selection; model selection; and performance estimation. Important classes of models are covered, including Gaussian mixtures, decision trees, SVMs, and neural networks. The course will have one midterm and a final project involving classification of data delivered early in the term. (Offered alternate years.)

Course objectives:
Students who complete this course should gain:

Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman
(See Book web page for errata)
Reference Texts:
Pattern Classification, by R. Duda, P. Hart and D. Stork;
Neural Networks for Pattern Recognition, by C. Bishop

Pre-requisite: Introductory Probability and Statistics (EE505 or undergrad equivalent)

Grading Policy
Homework: 40%
Exam: 30% (May 20, 2008)
Final Project: 30% (see Final Project Guidelines), Final Presentation June 10, 3:30-6:30

Grader: TBD

EE511 Course Information
This page is maintained by Mari Ostendorf (mo@ee) and Kevin Duh (kevinduh@u). Last updated on 2 June 2008.