Fall 2001 CS Course Listings

This file contains the Fall 2001 course listings for the computer science department.

Archive of past courses

Course Code: COMP527
Course Title: Pattern Recognition

Instructor: Dit-Yan Yeung
Room: 3545
Telephone: x6977
Email:
WWW page: http://cse.hkust.edu.hk/~dyyeung/

Area in which course can be counted: AI

Course description (can be more detailed than the one in the calendar):
Pattern recognition is one of the subareas of artificial intelligence (AI) with successful applications in many real-world domains. It is fundamental to such traditional research areas as character recognition, speech and language understanding, computer vision and image processing. Many techniques in pattern recognition are also becoming more and more useful to other areas, including information retrieval, data mining (or knowledge discovery in databases), electronic commerce, and decision support systems. The focus of this course is on statistical pattern recognition and its relationships with the related fields of machine learning and neural networks.

Course objective:
In this introductory postgraduate course, fundamental concepts and techniques background so that they can apply relevant techniques in disciplined ways to solve pattern recognition problems encountered in a variety of real-world applications.

Course outline/content (by major topics):
Bayesian decision theory
Parametric density estimation
Nonparametric density estimation
Discriminant functions
Feedforward neural networks
Feature selection and extraction
Clustering
Mixture density estimation
Hidden Markov models
Estimating and comparing classifiers
Combining classifiers

Text book:
R.O. Duda, P.E. Hart, and D.G. Stork (2001). Pattern Classification. 2nd Edition. Wiley.

Reference books/materials:
K. Fukunaga (1990). Introduction to Statistical Pattern Recognition. Academic Press.
S. Theodoridis and K. Koutroumbas (1998). Pattern Recognition. Academic Press.

Grading Scheme:
TBD

Pre-requisites/Background needed:
Background in linear algebra, probability and statistics

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A-

Course Code: COMP587
Course Title: Parallel Processing: Software

Instructor: Ishfaq Ahmad
Room: 3541
Telephone: x6980
Email:

Area in which course can be counted: CE

Course description:
pls. refer to the academic calendar

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: Instructor's consent

Course Code: COMP621C
Course Title: Advanced Topics in Artificial Intelligence: Readings on Logic-Based AI

Instructor: Fangzhen Lin
Room: 3511
Telephone: x6975
Email:
WWW page: http://cse.hkust.edu.hk/~flin

Area in which course can be counted: AI

Course description (can be more detailed than the one in the calendar):
An advanced AI course on Knowledge Representation and Reasoning. Topics covered will include first-order logic, nonmonotonic logics, action theories, and inductive logic programming.

Course objective:

Course outline/content (by major topics):
- Review of propositional and first-order logics
- Logic of knowledge
- Nonmonotonic reasoning
- Answer set logic programming
- Reasoning about actions and planning
- Learning first-order theories: inductive logic programming
- Student presentations.

Text book: None

Reference books/materials:
S. Russell and P. Norvig. Artificial Intelligence. Prentice Hall, 1995.
M. Genesereth and N. Nilsson. Logical Foundations of Artificial Intelligence. MKP. 1987

Grading Scheme:
35% Midterm, 35% for projects, and 30% for presentations, and presentation attendance.

Pre-requisites/Background needed:
An introductory course on AI

Available for final year UG students to enroll: Yes/No. No.

Minimum CGA required for UG students:

Course Code: COMP621D
Course Title: Advanced Topics in AI: Biometrics

Instructor: H.C. SHEN
Room: 3557
Telephone: 2358-6987
Email:
WWW page: http://cse.hkust.edu.hk/~helens/

Area in which course can be counted: AI

Course description (can be more detailed than the one in the calendar):
Identification (authentication) of individuals to gain access to classified information and/or secure systems has become an essential part of this modern networked society. This course introduces current biometric technologies which provide automatic authentication of individuals. Biometric systems utilize the physiological or behavioural characteristics of an individual for identification. We shall investigate and study the advantages and disadvantages of the various techniques, such as finger prints; voice; faces; hand geometry; iris; retina; and others.

Course objective:
TO understand the state-of-art of biometric technologies; to survey the current available biometric system on the market; to improve existing techniques; and to explore new techniques.

Course outline/content (by major topics):
- an overview of biometrics
- Existing biometric technologies:
- voice
- fingerprints
- faces
- hand geometry
- iris
- ear
- retina
- others
- Essentials of a biometric-based identification (authentication) system.
- Research issues in personal identification
- Issue of privacy
- and others

Text book: None

Reference books/materials:
1. A. Jain, R. Bolle, S. Pankanti, (Edit) "BIOMETRICS: Personal Identification in Networked Society", Kluwer Academic Publishers, 1999. (ISBN 0-7923-8345-1) TK7882.P3 B36
2. J. Ashbourn, "Biometrics: Advanced Identity Verificatioin", Springer-Verlag, 2000. (ISBN 1-85233-243-3) TK7882.P3 A84

Grading Scheme:
- class participation
- class presentation
- project

Pre-requisites/Background needed:
- knowledge in probability and statistics is essential;
- knowledge in pattern recognition and/or vision will be useful

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A-

Course Code: COMP630E
Course Title: Topics in Database Systems: Foundations of Database Systems
Abbreviated Title: Fnd. of Database Systems

Instructor: Wilfred Ng
Room:
Telephone:
Email:
WWW page:

Area in which course can be counted: Data and Knowledge Management

Course description (can be more detailed than the one in the calendar):
This course presents in-depth coverage of important aspects of the relational database model and semantic justification for database design. Some recent advances in relational database theory will also be introduced.

Course objective:
To equip students with the knowledge related to a variety of fundamental database topics. To introduce students some challenging problems arising in recent database research.

Course outline/content (by major topics):
1. Database Models and Data Abstraction Levels
2. Database Query Languages and their Expressive Power
3. Integrity Constraints in Relational Databases
4. Database Design and ERD Construction
5. Normal Forms and Database Decompositions
6. Concurrency Control
7. Recent Extensions in the Relational Data Model

Text book: "A Guided Tour of Relational Databases and Beyond" by Mark Levene and George Loizou, Springer Verlag, 1999

Reference books/materials: (tentative -- to be confirmed)
"A First Course in Database Systems" by Jeffery Ullman and Jennifer Widom, Prentice Hall, 1997

Grading Scheme:
To be determined

Pre-requisites/Background needed:
CS UG background

Available for final year UG students to enroll: NO

Minimum CGA required for UG students: N/A

Course Code: COMP641F
Course Title: Topics in Graphics: Introduction to Image Processing

Instructor: Michael Brown
Room: ??
Telephone: ??
Email:
WWW page: http://cse.hkust.edu.hk/~brown

Area in which course can be counted: ST

Course description (can be more detailed than the one in the calendar):
This course introduces basic theory and practices used in digital image processing. Topics to be covered include, image transforms, image enhancement, image compression, segmentation, and the basics of pattern recognition. An introduction to temporal image (video) processing will also be covered as time allows.

Course objective:
The course is intended to give students a broad overview and practical technical details to issues concerning digital image processing.

Course outline/content (by major topics): (tentative)
Overview of Digital Imaging
Image Geometry
Image Transforms
Image Enhancement/Restoration
Image Compression
Segmentation
Mathematical Morphology
Basics of Pattern Recognition
Introduction to Temporal Image Processing (Video)

Text book: (Tentative. To be confirmed later)
Gonzalez and Woods, "Digital Image Processing", Prentice Hall, ISBN: 0-201-50803-6

Reference books/materials:
Additional readings may be distributed in class.

Grading Scheme:
To be announced.

Pre-requisites/Background needed:
CS UG background

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the Instructor Required

Course Code: COMP670J
Course Title: Topics in theory: Approximation Algorithms

Instructor: Mordecai Golin
Room: 3559
Telephone: 2358-6993
Email:
WWW page: http://www..cse.ust.hk/~golin

Area in which course can be counted: Fundamentals of Computer Science

Course description and objective:
Most computer science researchers have given up on the possibility of finding techniques that always yield optimal solutions to NP-hard problems.

This still leaves us with the task of finding `good' solutions to these problems. The theory of approximation algorithms is devoted to developing techniques that yield provably good (`approximate') solutions to these problems.

In this course we will learn about the theory and practice of designing such algorithms. We will also, time permitting, study approximation in online algorithms such as cache paging.

Please see cse.hkust.edu.hk/~golin/ for an earlier version of this course.

Course outline/content (by major topics):
I. An introduction to approximation
II. Facility Location and Scheduling problems
III. The Shortest Superstring problem
IV. Polynomial Time Approximation Schemes
V. Randomized Approximation Algorithms
VI. Linear programming and LP-Duality based techniques
VI. Online algorithms (time permitting)

Text book:
Approximation Algorithms by Vijay Vazirani

Reference books/materials:
(1) Approximation Algorithms by Dorit Hochbaum
(2) Randomized algorithms / Rajeev Motwani, Prabhakar Raghavan
(3) Computers and intractability : a guide to the theory of NP-completeness Michael R. Garey and David S. Johnson
(4) Introduction to algorithms Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest
(5) Complexity and Approximation: Combinatorial Optimization Problems and their Approximability Properties G. Ausiello et al, editors.

Grading Scheme:
to be determined.

Pre-requites/Background needed:
No prerequisites. Background needed is COMP271 or equivalent and good grounding in mathematics, especially linear algebra and probability theory.

Available for final year UG students to enroll: Yes, but only with permission of the instructor.

Archive of past courses