Fall 2010: Pattern Recognition [CS688]

Professor:
Carlotta Domeniconi, Rm 4404 ENG, carlotta\AT\cs.gmu.edu

Prerequisites:
CS 580 or permission of instructor.
Programming experience is expected.
Students should be familiar with
basic probability and statistics concepts, linear algebra, optimization, and multivariate
calculus.

Location and Time:
We meet in IH 136, T 7:20pm  10:00pm
General Description and Preliminary List of Topics:
Pattern recognition is concerned with the automatic finding of regularities in data and with the use of these regularities to take actions, such as classifying images or documents into different categories.
The course covers key algorithms and theory at the core of pattern recognition.
Particular emphasis will be given to the statistical learning aspects of the field.
Topics include:
decision theory, Bayesian theory, curse of dimensionality, linear and nonlinear
dimensionality reduction techniques,
classification, clustering,
kernel methods, mixture models and EM, HMMs, ensemble methods.
Course Format:
Lectures by the instructor. Besides material from the textbook, topics not discussed in the book may also be
covered.
Research papers and handouts of material not covered in the book will
be made available.
Grading will be based on homework assignments,
exams, and a project. Homework assignments will require
some programming. Exams and homework assignments must be done on an individual basis. Any deviation from this policy will be considered a violation of the
GMU Honor Code.
Course Project:
The project gives students an opportunity to explore in depth a particular topic/area of the course. The topic of the project, of course, should be related to the material covered in class, but otherwise students are free to select the specific topic. Possible types of projects include:
An application research project: The project demonstrates the application of some techniques discussed in class in an application domain (e.g., text mining, bioinformatics, computer vision, image processing, artificial intelligence etc.). Properties, drawbacks, advantages of the used techniques are analyzed within the context of the explored application domain.
A theoretical or methodological research project: A study of different classes of models and approaches; proving either theoretically or experimentally properties of known algorithms; designing a new approach.