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Instructor Class Description

Time Schedule:

Jeffrey A. Bilmes
E E 596
Seattle Campus

Advanced Topics in Signal and Image Processing

Topics of current interest in signal and image processing. Content may vary from offering to offering. Prerequisite: permission of instructor.

Class description

Course Announcement Pattern recognition from a Graphical Models perspective EE596B Statistical Pattern Recognition II

Spring 2004 Instructor: Jeff A. Bilmes, 418 EE/CS Bldg. Email: bilmes@ee.washington.edu

Course web page: http://ssli.ee.washington.edu/courses/PRII Units: 4 Course Hours: TTh 3:30-5:20 EE1-037 ***NOTE***: First meeting will be Thursday, April 1st at 4:30pm. Due to seminar conflicts, the course will occasionally run from 4:30-6:20pm.

Description: This course will serve as a general introduction to graphical models and Bayesian networks for pattern recognition and statistical learning. Topics will include:

1) Graph semantics: conditional independence; semantics of directed and undirected models; algorithms for conditional independence including Bayes-ball, d-separation, Markov properties on graphs, factorization.

2) Models: linear Gaussian models, mixture models, factor analysis, probabilistic decision trees, Markov Random Fields, Gibbs distributions, conditional random fields, multivariate Gaussians as graphical models.

3) Dynamic (temporal) models: Hidden Markov Models, Kalman filtering and linear-Gaussian HMMs, dynamic Bayesian networks (DBNs)

4) Graph Theory: moralization; triangulated, decomposable, and intersection graphs, k-trees, hypergraphs.

5) Probabilistic Inference: junction trees, belief propagation and its guises (including Pearl formulation, Hugin, Shafer-Shenoy, Bucket-elimination, etc.); join-trees and data-strucure representations thereof; methods to optimally triangulate graphs; generalizations including mean-field and variational, loopy-propagation, and sampling.

6) Learning: learning Bayesian networks, EM for parameter and structure learning, alternating minimization, discriminative parameter and structure learning, other learning methods.

7) Models in practice: QMR and Factorial HMMs, turbo-coding and other codes on graphs (and the belief propagation algorithm), various dynamic models, etc. Practical issues of using graphical models and Bayesian networks for real-world problems.

We will also briefly introduce kernel machines, and topics such as bootstrapping, model averaging, bagging, and boosting.

The course will have homework assignments, a midterm exam, and a final project (a research paper and final presentation). There will be no final exam.

Course materials: Main Texts: 1) "An Introduction to Graphical Models", by Mike Jordan (we will use a pre-published version directly from the authors) 2) Online course reader

Other Texts:

1) Chapters from "Graphical Models" by Steffen Lauritzen, Oxford Science Publications

Reference Texts: 1) "Neural Networks for Pattern Recognition", by C. Bishop 2) "The Elements of Statistical Learning: Data Mining, Inference," and Prediction Hastie, Tibshirani, and Friedman 3) "Pattern Classification," R. Duda, P. Hart and D. Stork

Prerequisite: Statistical Pattern Recognition I, or prior exposure to pattern recognition concepts, or consent of instructor.

Student learning goals

General method of instruction

Recommended preparation

Prerequisites: basic probability, statistics, and random processes Basic knowledge of matlab. The course is open to students in all UW departments.

Class assignments and grading


The information above is intended to be helpful in choosing courses. Because the instructor may further develop his/her plans for this course, its characteristics are subject to change without notice. In most cases, the official course syllabus will be distributed on the first day of class.
Additional Information
Last Update by Jeffrey A. Bilmes
Date: 04/04/2004