Spring 2016: Pattern Recognition [CS688]

Professor: Carlotta Domeniconi, Rm 4424 ENG, carlotta\AT\cs.gmu.edu, Office hours: TBA

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

Location and Time: We meet in the Art and Design Building L008, M 4:30pm - 7:10pm

Textbook:

C. M. Bishop Pattern Recognition and Machine Learning, Springer, 2006.
Book's companion website

Useful material:

Overview on Linear Algebra

Andrew Moore's Tutorials: Collection of tutorials on topics of interest for this class
Course Web Page

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 non-linear 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.