Fall 2008 Graduate CSE Courses

Last Update: May 8, 2008

Note: NEW or UPDATED material is highlighted

THIS PAGE ONLY LISTS THOSE COURSES FOR WHICH INSTRUCTORS HAVE SENT ME COURSE DESCRIPTIONS.

FOR THE FULL LIST OF C.S.E. COURSES FOR Fall 2008, link to MyUB.

PLEASE CONTACT THE INSTRUCTOR FOR FURTHER INFORMATION ABOUT ANY OF THESE COURSES

CSE 663

TOPIC: ADVANCED TOPICS IN KNOWLEDGE REPRESENTATION & REASONING
INSTRUCTOR: William J. Rapaport
DAY & TIME: MWF 11:00-11:50 a.m.
DESCRIPTION: This course is a sequel to Prof. Shapiro's CSE 563 from the Spring 2008 semester. It will be a survey of issues and techniques of representing knowledge, belief, and information in a(n artificially intelligent) computer system and of the syntax and semantics of various representational formalisms. Classic papers will be read and current research issues discussed. I will begin with a brief review of logic and automated theorem proving (unification and resolution) and of the SNePS knowledge-representation, reasoning, and acting system. Remaining topics will include some or all of the following, as well as others as time permits: ontologies, semantic networks, production systems, frames, description logics, inheritance networks, default reasoning, and modal and epistemic logics.
PREREQUISITES: Official:
Graduate standing and either CSE 563 (Knowledge Representation) or CSE/LIN 567 (Computational Linguistics); or else permission of instructor.

Unofficial:
Knowledge of first-order logic, and some familiarity with resolution and unification (such as might have been obtained in a previous AI course, CSE 563, or—for unification, at least—in CSE 567). If you did not take CSE 563 in Spring 2008 and/or have no background in first-order logic, including unification and resolution theorem proving, then please see Prof. Rapaport before registering.
WEB PAGE: Will eventually be available here; till then, see the website for the previous incarnation of the course.

NEW

CSE 702: Seminar in Pattern Theory

Instructor: Jason Corso
Day and Time: TBA
Description: This seminar will focus on Grenander's Pattern Theory from a practical, contemporary perspective. Pattern Theory is the study of patterns from a representational perspective rather than a recognition one. Miller and Grenander write "Pattern theory attempts to provide an algebraic framework for describing patterns as structures regulated by rules, essentially a finite number of both local and global combinatory operations. Pattern theory takes a compositional view of the world, building more and more complex structures starting from simple ones. The basic rules for combining and building complex patterns from simpler ones are encoded via graphs and rules on transformations of these graphs." We will explore various theoretical aspects of modern pattern theory (e.g., probabilistic graphical models, grammars, matrix groups, information measures, manifolds, Markov processing and sampling) in the context of practical problems in computer vision and medical imaging. Students will be required to give one or two (depending on seminar size) prepared lectures during the semesters. Grading is S/U; letter grading is available as an option and requires a project.
PREREQUISITES: A working knowledge of computer vision, pattern recognition, and machine learning is suggested. Students are expected to know material in courses 555, 573, 574 and 672.
WEB PAGE:http://www.cse.buffalo.edu/~jcorso/t/2008F_702