Fall 2002 CS Course Listings

This file contains the Fall 2002 course listings for the computer science department.

Archive of past courses

Course Code: COMP524
Course Title: Computer Vision

Instructor: Chi-Keung Tang
Room: 3561
Telephone: 2358 8775
Email:

Area in which course can be counted: AI

Course description:

Introduction to techniques for automatically describing visual data and
tools for image analysis; perception of spatial organization; models of
general purpose vision systems; computational and psychological models
of perception.

Background needed: COMP221, knowledge in linear algebra

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: cs341 grade >= A- OR
Permission of the instructor
[email for appointment]

Course website: https://course.cse.ust.hk/comp524 (use your CSD username and password to log on)



Course Code: COMP530
Course Title: Database Architecture and Implementation

Instructor: Hongjun Lu
Room: 3543
Telephone: 2358 8773
Email:

Area in which course can be counted: DB

Course description:

Systems and architecture concepts in database management systems:
advanced storage and access methods; transaction processing; query
processing and optimization; implementation of relational operators;
memory and storage management; fault tolerance; recovery.

Background needed: COMP231 and COMP252



Course Code: COMP 538
Course Title: Reasoning and Decision under Uncertainty

Instructor: Nevin Zhang
Room: 3504
Telephone: 2358-7015
Email:

Area in which course can be counted: AI

Course description:

This course covers methodologies developed recently in the AI
community for dealing with the problem of complexity that arises when
applying probability theory. Fundamental to the methodologies is the
notion of conditional independence, which enables one to decompose a
complex system into simpler parts and thereby reduce complexity of
model construction and problem solving.
Bayesian networks deal with the complexity problem in probabilistic
reasoning. They represent the structure of a system using a directed
graph of random variables. Conditional independencies can be readily
identified from the graph and are used to drastically reduce the
complexity of inference. Model construction can be done manually
using the intuitively appealing graphical interface provided.
Alternatively, models can be learned from data via statistical
principles such as maximum likelihood estimation and Bayesian
estimation. The latter is the focus of much recent research and has
attracted much attention in the AI, machine learning, and data mining
communities.

Many of the classical multivariate probabilistic systems studied in
fields such as statistics, systems engineering, information theory,
pattern recognition and statistical mechanics are special cases of the
general graphical model formalism -- examples include mixture models,
factor analysis,hidden Markov models, Kalman filters and Ising models.
The graphical model framework provides a way to view all of these
systems as instances of a common underlying formalism. This view has
many advantages -- in particular, specialized techniques that have
been developed in one field can be transferred between research
communities and exploited more widely. Moreover, the graphical model
formalism provides a natural framework for the design of new systems.

Half of the course will focus on Bayesian networks. We will discuss
representation, inference, and learning issues. Two special models,
namely hidden Markov models and latent class analysis will also be
covered. The former is widely applied in pattern recognition while
the latter in social sciences. Moreover, we will discuss how causality
can be learned from data.

The objective of this course is to bring the students to the forefront
of Bayesian network research and application.

Although stated otherwise in the university calendar, decision theory
and reinforcement learning will not be covered so that the course will
not be overloaded.

Visit http://cse.hkust.edu.hk/~lzhang/comp538/ for more information.

Background needed: Knowledge of probability



Course Code: COMP630F
Course Title: Topics in Database Systems: Databases Meet Networks

Instructor: Qiong Luo
Room: 3552
Telephone: 2358-6995
Email:
WWW page: http://cse.hkust.edu.hk/~luo

Area in which course can be counted: DB (database)

Course description (can be more detailed than the one in the calendar):

The advances of computer networking technologies, especially the Internet,
have posed exciting challenges to the database area. This course introduces
the latest research results and industrial developments in the database
field that are addressing these challenges.

Course objective:

This course does not serve as a complete survey of the field; Rather, the
students are expected
-- to identify a few (existing or new) database problems related
to the networks,
-- to describe some (existing or new) solutions to these problems,
and
-- to demonstrate their (new) contributions in answering a couple
of research questions.

Course outline/content (by major topics):

Adaptive Query Processing
Database-Backed Web Sites
Internet Search Engines
Peer-to-Peer Systems
Querying over Streams
Wireless/Embedded DBMS
XML Query Processing

Text book: None

Reference books/materials:

[1] Ramakrishnan, Raghu et al.: Database Management Systems, 2nd Edition.
McGraw-Hill, 1999.
[2] Kurose, James F. et al.: Computer Networking: A Top-Down Approach
Featuring the Internet. Addison Wesley, 2001.
[3] Research papers will be made available through the course web page.

Grading Scheme:

Class participation, paper presentation, and course projects.

Pre-requisites/Background needed: None.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor



Course Code: COMP651C
Course Title: Topics in Computer Systems Analysis: Simulation and Performance Evaluation

Instructor: Gary Chan
Room: 3507
Telephone: 2358-6990
Email:

Area in which course can be counted: CE

Course description:

Advanced topics in computer system analysis; issues in the development
of system and network models; simulation techniques and output
analysis; variance reduction; queueing models and their applications;
recent developments in techniques and tools for system evaluation

This course aims to introduce tools and techniques in evaluating
computer networks. There will be 4 major topics, each of which
spanning about 3-4 weeks:

* Simulation methodology and theory

Principles of simulation, generation of uniform and random
variables, discrete-event simulation, output analysis, and
importance sampling

* Probability and random processes

Probability measure, conditional probability, expectations,
distribution functions, stochastic process (exponential and
Poisson)

* Markov properties and simple queues

Markov chain, birth-and-death process, Little's theorem, $M/M$
queues, $M/G/1$ queue

* Queuing networks and computer systems performance evaluation

Burke's theorem, Jackson's network, closed and open network of
queues, network design (capacity assignment, flow assignment,
etc.), analysis of MAC schemes (Aloha and CSMA)

Course pre-requisite: Probability and statistics; knowledge in
computer networks at the level of COMP361 and COMP362; interest in
performance modelling of computer systems



Course Code: COMP670M
Course Title: Topics in Theoretical Computer Science: Delaunay Mesh Generation

Instructor: Siu-Wing Cheng
Room: 3514
Telephone: 2358-6973
Email:

Area in which course can be counted: TH

Course description:
This course concentrates on three-dimensional Delaunay
triangulation and its application in generating
well-shaped Delaunay mesh for finite element analysis.
The first part of the course will cover the properties
of Delaunay triangulation of a point set and construction
algorithms. The second part of the course will cover
the Delaunay refinement algorithms for constructing
Delaunay meshes and elimination of slivers.



Course Code: COMP685A
Course Title: Topics in Applications of Computer Science: Cryptography

Instructor: Cunsheng Ding
Room: 3518
Telephone: 2358 7021
Email:
WWW page: http://cse.hkust.edu.hk/faculty/cding

Area in which course can be counted: APP (Applications of CS)

Course description (can be more detailed than the one in the calendar):

This course introduces the basic tools for computer security, network
security, and data security. It mainly covers data confidentiality,
data integerity, user and data authentication, key management, and
cryptographic protocols.

Course objective:

The objective of this course is to introduce the basic cryptographic
tools for information security, data security, and systems security,
which are essential for students who wish to work on data and systems
security, electronic commerce, and networking.

Course outline/content (by major topics):

History of cryptography, classical systems, block and stream ciphers,
public-key cryptography, hash functions, digital signature, user and
data authentication, nonrepudiation, data integrity, secret sharing,
key management, cryptographic protocols, and email security.

Text book:

No textbook, but detailed lecture notes will be provided.

Reference books/materials:

1. William Stallings, Cryptography and Network Security, 2nd Edition,
Prentice Hall, 1999
2. D.R. Stinson, Cryptography Theory and Practice, CRC Press, 1995

Grading Scheme:

Assignments, midterm quiz, plus a project report. No final exam.
Detailed grading scheme will be anounced later.

Background needed:

Discrete mathematics. This course invoves a lot of mathematical topics.
Those who wish to take this course should have a good math capability.

Available for final year UG students to enroll: No

Minimum CGA required for UG students: N.A.
(can be `permission of the instructor')

Archive of past courses

Last updated by Lau Wai Kay on 19/08/2002.