CS 688 - Fall   2011

Pattern Recognition  – 70633 – CS 688 - 001

Instructor:  Prof. Harry Wechsler wechsler@gmu.edu

Course Description Explores statistical pattern recognition and neural networks. Pattern recognition topics include Bayesian classification and decision theory, density (parametric and nonparametric) estimation, linear and nonlinear discriminant analysis, dimensionality reduction, feature extraction and selection, mixture models and EM, and vector quantization and clustering. Neural networks topics include feed-forward networks and back-propagation, self-organization feature maps, and radial basis functions. Course emphasizes experimental design, applications, and performance evaluation.

Objectives – Theory and Practice at the interface between Machine Learning, Neural Networks, and Pattern Recognition to develop reliable and robust applications geared for the authentication, classification, detection, and discrimination of meaningful signals and patterns.

Prerequisite: CS 580 or equivalent.

Time, Day, and Venue: T – Tuesday, 7:20 pm – 10:00 pm, Krug Hall 7

Office Hours:  Tuesday, 6:15 – 7:15 pm (ENGR - 4448)

http://registrar.gmu.edu/calendars/2011Fall.html

First day of classes: Tuesday, August 30

Columbus Day break:  no class on Tuesday, October 11

Midterm (“closed books and notes”): Tuesday, October 25

Last day of classes: Tuesday, December 6

http://registrar.gmu.edu/calendars/2011FallExam.html

Final Exam: Tuesday, December 13, 7:30 – 10:15 pm

Grade Composition: 100%

- homework: 25%

- midterm: 25%

- term paper: 25%

- FINAL: 25%

 

Textbook:  (1) Theodoridis and Koutroumbas, Patter Recognition, 4th ed.;

and (2) Theodoridis, Pikrakis, Koutroumbas, and Cavouras, Introduction to Pattern Recognition – A MATLAB Approach --- Academic Press.

 

Tentative Schedule:

 

Lectures 1 - 2: BAYES DECISION THEORY and DENSITY ESTIMATION ~ August 30 ~ September 6

Textbook /Slides: Chaps. 1 - 2

MATLAB: Chap. 1

Texas A&M / Slides: 1-3, 14

Papers: TBD

Homework:  TBD

 

Lecture 3 - 4: LINEAR CLASSIFIERS ~ September 13 - 20

Textbook / Slides: Chap. 3

MATLAB: Chap. 2

Texas A&M / Slides: 4 – 5, 13, 17

Papers: PERFORMANCE EVALUATION

Homework: TBD

 

Lecture 5 - 6: NON-LINEAR CLASSIFIERS and NEURAL NETWORKS ~ September 27 – October 4

Textbook / Slides: Chap. 4

MATLAB: Chap. 2

Texas A&M / Slides:  18 – 20, 25

Papers: TBD

Homework:  TBD

 

Columbus Day Break~ Tuesday, October 11

 

Lecture 7: REVIEW ~ October 18

 

Lecture 8: MIDTERM ~ October 25

 

Lecture 9 - 10: FEATURE SELECTION ~ November 1 - 8

Textbook / Slides: Chap. 5 - 6

MATLAB: Chap. 3

Texas A&M / Slides: 9 – 10, 27

Papers: BIOMETRICS

Homework:  TBD

 

Lecture 11 – 12: HIDDEN MARKOV MODELS (HMM) ~ November 15 - 22

Textbook / Slides: Chap. 9

MATLAB: Chap. 6

Texas A&M / Slides: 23 - 24

Papers: COMPUTATIONAL LINGUISTICS and NATURAL LANGUAGE PROCESSING (NLP)

Homework:  TBD

 

Lecture 13 – 14: CLUSTERING & REVIEW ~ November 29 - December 6

Textbook / Slides: Chaps. 12 - 15

MATLAB: Chap. 7

Texas A&M / Slides: 16

Papers: SPECTRAL CLUSTERING and Web Cam Tracking / Surveillance

Homework: TBD

 

TERM PAPER~ Tuesday, December 6

 

ACADEMIC INTEGRITY
GMU is an Honor Code university; please see the University Catalog for a full
description of the code and the honor committee process. The principle of academic
integrity is taken very seriously and violations are treated gravely. What does academic integrity mean in this course? Essentially this: when you are responsible for a task, you will perform that task. When you rely on someone else’s work in an aspect of the performance of that task, you will give full credit in the proper, accepted form. Another aspect of academic integrity is the free play of ideas. Vigorous discussion and debate are encouraged in this course, with the firm expectation that all aspects of the class will be conducted with civility and respect for differing ideas, perspectives, and traditions. When in doubt (of any kind) please ask for guidance and clarification.


GMU EMAIL ACCOUNTS
Students must use their Mason email accounts—either the existing “MEMO” system or a new “MASONLIVE” account to receive important University information, including messages related to this class. See http://masonlive.gmu.edu for more information.


OFFICE OF DISABILITY SERVICES
If you are a student with a disability and you need academic accommodations, please see me and contact the Office of Disability Services (ODS) at 993-2474. All academic accommodations must be arranged through the ODS. http://ods.gmu.edu


OTHER USEFUL CAMPUS RESOURCES:


WRITING CENTER: A114 Robinson Hall; (703) 993-1200; http://writingcenter.gmu.edu


UNIVERSITY LIBRARIES “Ask a Librarian”
http://library.gmu.edu/mudge/IM/IMRef.html


COUNSELING AND PSYCHOLOGICAL SERVICES (CAPS): (703) 993-2380;
http://caps.gmu.edu

 

UNIVERSITY POLICIES
The University Catalog, http://catalog.gmu.edu, is the central resource for university
policies affecting student, faculty, and staff conduct in university academic affairs. Other
policies are available at http://universitypolicy.gmu.edu/. All members of the university
community are responsible for knowing and following established policies.