Spring 2002 CS Course Listings

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

Archive of past courses

Course Code: COMP526
Course Title: Natural Language Processing
Abbreviated Course Title: Natural Lang. Processing

Instructor: Dekai Wu
Room: 3539
Telephone: 2358-6989
Email:
WWW page: http://cse.hkust.edu.hk/~dekai and http://www.isilk.com

Area in which course can be counted: AI

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

Over the past five years, NLP has clearly emerged as one of
this decade's highest-impact areas of computer technology in
the real world as well as in research. NLP has become the
key technology that enables much of Knowledge Management,
Data Mining, Document Management, Speech Recognition,
the Semantic Web and next-gen Information Retrieval, and more.
According to industry expert studies by the Gartner Group,
NLP technologies represent HALF of the 12 most important
growth areas of computer technology during 2001-2010.
Even in today's lean economies, Merrill Lynch has just reported
that 96% of software decision makers across all industry sectors
will accelerate or continue their high investments in NLP
technology during 2002. It is difficult to exaggerate the
importance of this core information infrastructure technology.

The excitement behind this area will be investigated in this course.
The field has grown so explosively that this is the first offering of
the course where up-to-date text and reference books exist.
We will explore application of both statistical modeling techniques
(for which the Human Language Technology Center at HKUST
is particularly recognized) and symbolic knowledge-based
modeling techniques. We will share insights from both the
theoretical perspectives as well as the applied perspectives,
where we will leverage experiences from the HKUST startup
iSilk which has grown into a leading local NLP company.

NLP is a rich, challenging, and rewarding field that sits at the
intersection of science, technology, and psychology. It is for
those who value a broad, wide-ranging perspective but are
not afraid of drilling deep at the same time. I look forward to
meeting those of you who are interested in such directions.

Course objective:

- To establish a broad perspective over the foundations of NLP.
- To understand the methods, issues, and techniques via case studies.
- To learn hands-on how to turn theory to application.

Course outline/content (by major topics):

- NLP is an extremely broad area. Specific key topics and cases
across lexical, syntactic, semantic, and contextual processing
will be determined according to the class composition.

Text book:

- Foundations of Statistical Natural Language Processing, by
Christopher D. Manning & Hinrich Schutze. (June 1999)

Reference books/materials:

- Handbook of Natural Language Processing, edited by Robert
Dale, Hermann Moisl, & Harold Somers. (July 2000)
- Speech and Language Processing: An Introduction to Natural
Language Processing, Computational Linguistics and Speech
Recognition, by Daniel Jurafsky & James H. Martin. (Jan 2000)
- Readings and cases

Grading Scheme:

- 35% Class participation
- 65% Project

Background needed:

- Will adjust to class composition.
- Background in any AI or statistical areas will help you get more
out of the class.

Available for final year UG students to enroll: Instructor's consent.

Minimum CGA required for UG students: Instructor's consent.



Course Code: COMP537
Course Title: Knowledge Discovery in Databases
Abbreviated Course Title: Data Mining

Instructor: Qiang Yang
Room: 3652
Telephone: 2358-8768
Email:
WWW page: http://cse.hkust.edu.hk/~qyang/Teaching/537

Area in which course can be counted: DB

Course description:

Data mining has emerged as a major frontier field of study in recent years.
Aimed at extracting useful and interesting patterns and knowledge from large
data repositories such as databases and the Web,
data mining has successfully integrated techniques from the fields of
databases, statistics and AI.
This course will provide a broad overview of the field, preparing the students
with the ability to conduct research in the field.

Course objective:

- To get a broad understanding of data mining and knowledge discovery in
databases.
- To understand major research issues and techniques in this new area and
conduct research.
- To be able to apply data mining tools to practical problems.

Course outline/content (by major topics):

The data mining process
Decision trees
Rule induction
Instance-based learning
Bayesian learning
Clustering
Association rules
Web log analysis
Sequential data mining
Mining user preferences for recommendation systems

Text books:

1. Data Mining -- Practical Machine Learning Tools and Techniques with Java
Implementations by Ian Witten and Eibe Frank, Morgan Kaufmann Publishers.

2. Data Mining -- Concepts and Techniques by Jiawei Hana and Micheline Kamber.
Morgan Kaufmann Publishers.

Reference books/materials:

1. Principles of Data Mining. By David Hand, Heikki Mannila and Padhraic
Smyth. The MIT Press.

2. Selected Research Papers

Grading Scheme: Assignments and Midterm Exam (40%), Class Presentation (10%),
Final Project (50%)

Background needed: Database Systems, Machine Learning or Pattern Recognition

Available for final year UG students to enroll: No.



Course Code: COMP 573
Course Title: Computational Geometry

Instructor: Sunil Arya
Room: 3509
Telephone: 2358-8769
Email:

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

Course description:

This is an introductory course in Computational Geometry.
It deals with the design and analysis of algorithms for
manipulating geometric objects. Examples of objects
to be studied include Convex hulls, Voronoi diagrams,
and Triangulations.

Course objective:

This is an advanced algorithms course, whose goal is the study of
discrete geometric problems from a computational point of view. These
problems arise in many different areas such as Geographic Information
Systems, Computer Aided Design, Computer Graphics, and Robotics.

Course outline/content (by major topics):

Convex Hulls
Line Segment Intersection
Polygon Triangulation
Linear Programming
Orthogonal Range Searching
Point Location
Voronoi Diagrams
Arrangements and Duality
Delaunay Triangulations
Visibility Graphs

Text book:
M. de Berg, M. van Kreveld, M. Overmars and O. Schwarzkopf, Computational
geometry---algorithms and applications, Springer-Verlag, 1997.

Reference books/materials:

Lecture notes by David Mount. To be distributed.

H. Edelsbrunner, Algorithms in combinatorial geometry, Springer-Verlag, 1987.
On reserve in library.

F. P. Preparata and M. I. Shamos, Computational geometry: an introduction,
Springer-Verlag, 1985. On reserve in library.

K. Mulmuley, Computational geometry: an introduction through randomized
algorithms, Prentice-Hall, 1994. On reserve in library.

J. O'Rourke, Computational geometry in C, Cambridge University Press, 1994.
On reserve in library.

Grading Scheme:

3-5 written assignments 30%
Midterm exam 30%
Final exam 40%

Pre-requisites/Background needed:
Background needed: COMP271

Available for final year UG students to enroll: Yes.

Mimimum CGA required for UG students: Permission of instructor required.



Course Code: COMP610E
Course Title: Topics in SE: Software Development of E-Business Applications
Abbreviated Course Title: Soft. Dev. eBusiness Appl

Instructor: S.C. Cheung
Room: 3554
Telephone: 7016
Email:
WWW page: http://cse.hkust.edu.hk/~scc

Area in which course can be counted: ST

Course description:
This advanced course will cover the latest technologies in the development
and management of e-business applications. These technologies typically
include object-oriented modeling, CRM, software infrastructures, security,
enterprise information modeling and system planning. The course will
include case studies and project presentation.

Course objective:
To understand the underlying enabling technologies of e-business
applications and to adopt a rigorous approach in the software development
of these applications.

Course outline/content (by major topics):

- Models for E-Business
- Object-Oriented Software Development
- Component-based Technologies
- Development of XML Applications
- e-Business Security
- Customer Behavior Models
- Capacity Planning Methodology
- Performance Modeling
- Workload Characterization
- mCommerce Applications
- Case Studies & Project Presentation

Text book:

Reference books/materials:

Grading Scheme:
Participation (10%)
Readings / Course Assignment (30%)
Project (60%)

Background Needed: Java, software engineering concepts, database, networking

Available for final year UG students to enroll: No



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:
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:
- participation in class
- presentation in class
- 1 assignment
- 1 quizz
- 1 project (in groups)

Pre-requisites/Background needed:
- Pattern Recognition (comp527) or
- Computer Vision (comp524) or
- Introduction to Image Processing (comp641F)

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A-



Course Code: COMP621E
Course Title: Advanced Topics in AI: Medical Image Analysis and Processing
Abbreviated Course Title: Med Image Anal&Processing

Instructor: Dr. Albert C. S. Chung
Room: ?
Telephone: ?
Email:
WWW page: Under construction, will be available before class begins.

Area in which course can be counted: AI

Course description:
This course introduces basic computational techniques for medical image
analysis and processing.

Course objective:
The objective is to give students a board overview of theory and practice
issues of the current and commonly used algorithms in this field. Some
sophisticated image processing and analysis tools and state-of-art may
also be introduced subject to the availability of time.

Course outline/content (by major topics) (tentative):
1. Introduction to medical imaging
2. Segmentation of medical images
3. Multi-modality registration
4. Fundamental enhancement techniques

Text book:
None

Reference books/materials:
Course materials will be distributed in class

Grading Scheme:
to be determined

Pre-requisites/Background needed:
No Pre-requisites. Sound background in engineering mathematics is helpful,
especially probability theory and statistics.

Available for final year UG students to enroll:
Yes.

Minimum CGA required for UG students: Permission of the instructor is required



Course Code: COMP621F
Course Title: Advanced Topics in AI: Speech Recognition: Theory & Applications
Abbreviated Course Title: Speech Recognition

Instructor: Dr. Brian Mak
Room: 3513
Telephone: x7012
Email:
WWW page: http://cse.hkust.edu.hk/~mak

Area in which course can be counted: Artificial Intelligence

Course description:

The course is an introduction to Speech Recognition, with an emphasis on
acoustic modeling. It will be treated as a general data modeling problem
so that various modeling techniques learned in this course may be
applied to other types of pattern recognitions as well. Data modeling is
also treated as an optimization problem and various
constrained/uncontrained optimization techniques will be applied.

Course objective:

An introduction to Speech Recognition, with an emphasis on acoustic
modeling and decoding algorithm. Modeling and optimization techniques
include: maximum likelihood estimation, EM algorithm, constrained
optimization, Lagrange multiplier, hidden Markov modeling, neural
network, decision tree, support vector machine, and discriminative
training. Speaker daptation methods include: MLLR, MAP, and eignevoice.

Course outline/content (by major topics):

1. A review of pattern classification.
2. Speech production and feature extraction.
3. Function fitting
4. Hidden Markov modeling
5. Neural network
6. Decision tree
7. Support vector machine
8. Discriminative training
9. Speaker adaptation

Textbook:
X. Huang, A. Acero, H.W. Hon, "Spoken Language Processing", Prentice
Hall, 2001.

References:
R.O. Duda, P.E. Hart, D. G. Stork, "Pattern Classification", 2nd Ed.,
Wiley-Interscience, 2000.

L. Rabiner, B.H. Juang, "Fundamentals of Speech Recognition", Prentice
Hall, 1993.
N. Gershenfeld, "The Nature of Mathematical Modeling", Cambridge
University Press, 1999.

Grading Scheme:

assignments, final, projects, and paper presentation.

Pre-requisites/Background needed:
Background: probability, statistics, calculus, pattern classification

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

Minimum CGA required for UG students: 3.5
(can be `permission of the instructor')



Course Code: COMP621G
Course Title: Advanced Topics in AI: Computer Vision & Image-based Rendering
Abbreviated Course Title: CVIBR

Instructor: Long QUAN
Room: 3552
Telephone: 2358 7018
Email:
WWW page: http://cse.hkust.edu.hk/~quan/

Area(s) in which course can be counted: AI and ST
(note: this course can satisfty either an AI requirement or a ST one during graduation check but not both)

Course description:

This course first provides a broad overview of the theory and practice of
3D computer vision which is about how to obtain 3D structure from 2D images.
Then we will describe its application fileds in computer graphics and multimedia.
Particularly we will focus on recent developements for image-based modelling
and rendering to which computer vision is closely related. The classical projective
geometric as its mathematical foundation will be introduced and used throughtout
the course.

Course objective:
fundamental 3D computer vision concepts,
fundamental geometric concepts,
and basic image-based modelling and rendering for computer graphics application.

Course outline/content (by major topics):

Introduction to projective geometry,
multi-view geometry,
3D reconstruction,
image-based rendering.

Text book:
Multiple View Geometry in Computer Vision, Richard Hartley and Andrew
Zisserman

Reference books/materials: TBA

Grading Scheme: TBD

Background needed: :
some linear algebra and image processing background

Available for final year UG students to enroll: Yes

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



Course Code: COMP660G
Course Title: Mobile networking

Instructor: Dr. Jelena Misic
Room: 3524
Telephone: 2358 8767
Email:
WWW page: http://cse.hkust.edu.hk/~jmisic

Area in which course can be counted: Computer Engineering

Course description:

This course is focused on the technological and research issues in
today's wireless Internet. It discusses its networking infrastructure,
protocols, and applications. It also discusses some of the research
topics in this area.

Wireless cellular voice communications are now in the mature phase of
their second generation. Examples of second generation digital cellular
technology are GSM and IS-95 systems. Recently, second generation
cellular networks are being enhanced in order to support Internet
services. The example is GPRS (General Packet Radio Service) network
developed using the existing GSM infrastructure. Also, research and
development activities are ongoing in the area of the third generation
wireless systems. The representative communication technologies for 3G
systems are cdma2000 and WCDMA. Second type of wireless infrastructure
intended for data and voice users are wireless local area networks.
Although they were initially designed for data applications, most of
them today support multimedia applications. Third level of wireless
infrastructure is comprised of small scale entreprise and home networks
which can handle voice and data services.

Related to the infrastructure is the issue whether mobile stations
communicate via the base station (in the cellular environment) or they
communicate directly to each other (in ad-hoc network). Ad hoc
networking imposes routing problems due to the changing network
topology and lack of fixed routers.

Together with communication infrastructure, protocol architectures
like Mobile IP and WAP (Wireless Application Protocol) are developed to
support Web applications on mobile terminals with limited dimensions,
battery power, and limited bandwidth of the wireless communication
path. Technical solutions for interconnecting wired Voice over IP
applications with wireless cellular networks have also emerged. Also,
together with the standard Internet applications, some auxiliary mobile
applications like active badges, sentinent computing and geo-location
systems are introduced in the mobile computing community.

Course objective:

This course should give the overview of the state of the art
in mobile networking and point to some research challenges in
this area.

Course outline/content (by major topics):
1. Basics about propagation of radio waves
2. Cellular wireless networks. Basics about TDMA and spread spectrum (CDMA)
3. Cordless systems
4. Mobility management
5. Basics about GSM and GPRS, VoIP for mobile networks
6. Third generation mobile services.
7. Mobile IP and Wireless Application Protocol
8. Wireless LAN technology, IEEE 802.11, HyperLan
9. Bluetooth
10. Mobile ad hoc networks

Text book:

Reference books/materials:

1. William Stallings, ``Wireless communications and networks",
Prentice Hall, 2002, ISBN 0-13-040864-6
2. Yi-Bing Lin, Imrich Chlamtac, ``Wireless and Mobile Network Architecture"
John Wiley & Sons, 2001, ISBN 0-471-39492-0
3. Charles E. Perkins editor, ``Ad Hoc Networking",
Addison-Wesely, 2001, ISBN 0-201-30976-9
4. A number of technical papers will be provided to illustrate novel
concepts.

Grading Scheme:
2 x 25% quizzes
20% class presentation
30% course project

Pre-requisites/Background needed:
comp361 or equivalent

Available for final year UG students to enroll: No.

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



Course Code: COMP680E
Course Title: Topics in CE: High-Speed Internet Switches and Routers
Abbreviated Course Title: Internet Routers

Instructor: Dr. Mounir Hamdi
Room: 3537
Telephone: x 6984
Email:
WWW page: http://cse.hkust.edu.hk/~hamdi

Area in which course can be counted: CE

Course description:

The focus of the course is on the design and analysis of high-performance
electronic/optical switches/routers
needed to support the development and delivery of advanced network services
over high-speed Internet.
Switches and routers and the KEY building blocks of the Internet, and as a
result, the capability of the
Internet in all its aspects depends on the capability of its switches and
routers. The goal of the course is to provide
a basis for understanding, appreciating, and performing research and
development in networking with a special
emphasis on switches and routers. The course begins with a survey of the
state of the art in switching and routing.
Then, it examines the issues involved in designing switches and routers for
both the optical domain and the
electronic domain. The issues include protocols, architectures, algorithms,
and performance evaluation.

The course involves both a lecture component and project component. Projects
will consist of practical designs of
switches/routers and will typically be executed by a small teams (2-3
people). During the first few weeks of the course
we will suggest a number of possible areas and projects. Teams should submit
formal project proposals. The projects will
require a mid-semester status report and a demo and final report at the end
of the semester.
The course evaluation is based on a the project and a mid-term exam.

The course will rely on lecture notes, research papers, and book chapters to
be provided to the students.

Course objective: The Objective of this course is to privide the basis for
understanding, appreciating, and performing
research and development in advanced networking with a special emphasis on
switches and routers.

Course outline/content (by major topics):

1. Introduction to High-speed networking
2. Architectures of electronic switches/routers
3. Architectures of optical switches/routers
4. Arbitration algorithms for electrnic/optical switches
5. Active queue management and congestion control for high-speed routers
6. Quality of service and differentiated services scheduling algorithms
7. Network processors: packet classification and router lookup
8. Multiprotocol Label Switching (MPLS) for Optical Internet

Text book:

Reference books/materials: Papers and book chapters to be put online for
student access

Grading Scheme: Project: 60%; Midterm exam: 20%; Homeworks: 20%.

Pre-requisites/Background needed: Introduction to Computer Networks

Available for final year UG students to enroll: No.

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

Archive of past courses