Henry S. Baird Fall 2007 Course
Pattern Recognition CSE 326/426
CRNs: 43875 (326); 43876 (426)
Note to CSE PhD Graduate Students: this course fulfills two 'Core Areas': (1) Computer Applications, and (2) Theory.
Note to undergraduates: you are very welcome in this course; you'll do the same HWs, programming exercises, and exams as the grad students, but you'll report on fewer research papers.
An introduction to the state of the art of pattern recognition and document image processing, and the machine-learning theory, algorithms, and systems architectures that underlie them.
Theoretical topics will include Bayesian decision theory, nonparametric methods, linear discriminant functions, neural nets, and algorithm-independent machine learning. Engineering challenges--- including trainable classification, segmentation, contextual analysis, autonomous adaptation, and 'anytime' algorithms---will be illustrated by high-performance computer vision systems selected mainly from document image analysis applications.
Weekly written homeworks or short programming exercises. A midterm exam. Students will select a set of related research papers (or a dissertation) from the recent literature and present a short talk in class summarizing and critiquing them. There is a choice between (1) a final exam or (2) a software project on a cutting-edge research problem from digital libraries or Web security (e.g. CAPTCHAs: vision-based Turing tests to tell computers and humans apart).
On completing this course, students will be sufficiently familiar with the theory, notation, and vocabulary of pattern recognition and machine learning to be able to pursue matters of interest in the current technical literature. They will also have a grasp of key engineering issues arising in applications.
Further, this course serves as an introduction to the state of the art of Document Image Analysis which is an essential technology in digital libraries, web-based search of scholarly materials, intelligence analysis, office automation, and web-based security. These topics are being actively researched in Lehigh's Pattern Recognition Research laboratory.
Textbook: Pattern Classification (2nd Ed.), R. O. Duda, P. E. Hart, & D. G. Stork, John Wiley & Sons, October 2000. 680 pages. ISBN 0-471-05669-3.
Lectures: Tuesdays & Thursdays, 9:20-10:35, Maginnes Hall 103. First meeting: Tuesday, August 28. (Last: Thursday, Dec. 6.)
Instructor: Prof. Henry Baird, firstname.lastname@example.org; office: Packard Lab 514C. Office Hour: <to be determined>
Prerequisites:CSE 340: Algorithms -- or comparable background in basic algorithms and data structures.
Math 205: Linear Algebra etc -- or similar familiarity with linear algebra & matrices.
Math 231 or Math 309 or CSC 450: Applied Probability & Statistics -- or some background in discrete probability and applied statistics & data analysis.
CSE 109: Programming in C++ -- or enough experience with C++, Java, C, or MatLab to complete a small software project without faculty supervision.
Accommodations for Students with Disabilities: If you have a disability for which you are or may be requesting accommodations, please contact both your instructor and the Office of Academic Support Services, University Center 212 (610-758-4152) as early as possible in the semester. You must have documentation from the Academic Support Services office before accommodations can be granted.
If you have any questions about prerequisites, ask the instructor: email@example.com.