Spring 2004 CS Course Listings

This file contains the Spring 2004 course listings for the Department of Computer Science.

Archive of past courses

Course Code: COMP512
Course Title: Advanced Distributed Software Development

Instructor: S.C. Cheung
Telephone: x7016
Email:
WWW page: http://cse.hkust.edu.hk/~scc/

Area in which course can be counted: Software & Applications

Course description:

Introduction to selected advanced concepts of software development in distributed environments. Topics include models and analysis, object-oriented methodologies for enterprise applications, web technologies for building e-business systems.

Course objective:

To understand the underlying principles of distributed software systems and to adopt a rigorous approach in the development of such systems.

Course outline/content (by major topics):

  • Object-oriented methodologies
  • Meeting today's challenges
  • Overview of UML
  • Object design patterns and software evolution
  • Architecture

  • Fundamental concepts and models
  • Introduction to concurrent programs
  • Process and threads
  • Concurrent execution
  • Shared objects and mutual exclusion
  • Monitors and condition synchronization
  • Deadlock
  • Safety and liveness properties
  • Model-based design

  • Concepts and technologies of e-Business applications
  • Basic concepts of e-Business integration
  • Models, standards and technologies
  • Web services

Text book:

  • J. Magee & J. Kramer. Concurrency - State models and Java Programs. John Wiley & Sons

Grading Scheme:

  • Participation (10%)
    • Participation is based on good questions, good answers, and active
    • participation in class discussions.
  • Programming Assignment(s) (40%)
  • Examination (50%)

Background needed:

UML, Java, Operating Systems, Automata theory, XML

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A-

Course Code: COMP537
Course Title: Knowledge Discovery in Data Bases

Instructor: Qiang Yang
Room: 3562
Telephone: X8768
Email:
WWW page: http://cse.hkust.edu.hk/~qyang/

Area in which course can be counted: Database

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 database, 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 get involved in small scale research projects.

Course outline/content (by major topics):

  • The data mining process
  • models and model evaluation
  • classification and prediction
  • clustering and association analysis
  • Data mining and information retrieval, Web and wireless data mining
  • Data mining and CRM
  • Data mining and user modeling
  • Other applications (bioinformatics, intrusion detection)

Reference books/materials:

  • Data Mining -- Practical Machine Learning Tools and Techniques with Java Implementations by Ian Witten and Eibe Frank, Morgan Kaufmann Publishers.
  • The Elements of Statistical Learning -- Data Mining, Inference and Prediction. T. Hastie, R. Tibshirani and J. Friedman. Springer. 2001
  • Data Mining -- Concepts and Techniques by Jiawei Han and Micheline Kamber. Morgan Kaufmann Publishers.

Grading Scheme:

  • Programming Assignments -- 30%
  • Midterm Exam -- 30%
  • Class Presentation -- 5%
  • Final Exam -- 35%

Background needed:

Statistics and programming skill (C++ or Java), algorithm analysis

Available for final year UG students to enroll: No.

Course Code: COMP581
Course Title: Cryptography and Security

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

Area in which course can be counted: Software & Applications

Course description:

This course gives an in depth coverage of the theory and applications of cryptography, and system security. In the cryptography part, basic tools for building security systems are introduced. The system security part includes electronic mail security, IP security, firewalls, Web security, and secure electronic transaction.

This course will be delivered in lecture mode. It will involve one course project.

Course objective:

After completion of this course, students will display a breadth of knowledge of both the principles and practice of cryptography and system security, and master basic tools for building security systems.

Course outline/content (by major topics):

Block and stream ciphers, public-key cryptography, hash functions, digital signature, user and data authentication, nonrepudiation, data integrity, public key infrastructure, secret sharing, key management, and cryptographic protocols; electronic mail security, IP security, Firewalls, Secure Sockets Layer, Secure Electronic Transaction.

Text book:

  • William Stallings, Cryptography and Network Security: Principles and Practices, 3rd Edition, Prentice Hall, 2003.

Reference books/materials:

  • D. R. Stinson, Cryptography Theory and Practice, CRC Press, Inc., 1995

Grading Scheme:

Assignments, quizzes, and course project.

Background needed:

Basic knowledge of computer networks

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: Permission of the instructor

Course Code: COMP621I
Course Title: Advanced Topics in AI: Advanced Topics in Machine Learning

Instructor: Dit-Yan Yeung
Room: 3541
Telephone: x6977
Email:
WWW page: http://cse.hkust.edu.hk/~dyyeung/

Area in which course can be counted: Artificial Intelligence

Course description:

The ability to learn is central to human and machine intelligence. Machine learning is playing an increasingly important role in both artificial intelligence (AI) and other areas, including planning, speech and language understanding, computer vision, computer graphics, information retrieval, knowledge discovery and data mining, bioinformatics, electronic commerce, and decision support systems. Building on top of prior background in some fundamental topics and techniques of machine learning, this research-oriented course will expose students to some recent research topics in the field. Under the guidance of the instructor, students will learn to do machine learning research and will write up a research paper on a selected topic as a term project.

Course objective:

The objective of this advanced topics course is to help research postgraduate students to keep abreast of some latest developments in machine learning research as well as some novel applications that are made possible by the newly developed tools. Active participation of students is expected. This course is not only useful to students working in machine learning, but is also useful to those working in other areas to apply advanced machine learning methods to the problems that they are working on.

Course outline/content (by major topics):

Major topics include boosting, semi-supervised learning, manifold learning, spectral methods, Bayesian methods, metric learning, kernel methods, and kernel learning.

Reference books/materials:

Many recent research papers

Grading Scheme:

Class participation, class presentation, and research paper writing

Background needed:

Background in machine learning or pattern recognition (equivalent to COMP 522 and COMP 527)

Available for final year UG students to enroll: No

Course Code: COMP 621J
Course Title: Advanced Topics in AI: Statistical Machine Translation

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

Area in which course can be counted: Artificial Intelligence

Course description:

Statistical methods for Machine Translation have become extremely complex and employ increasingly sophisticated combinations of machine learning techniques. In this course, we will explore in depth some of the more advanced techniques, including various decoding and heuristic search approaches, minimum Bayes Risk models, word graphs, compilation, and confidence measures.

Course objective:

  • To explore advanced techniques in the latest statistical machine translation models.
  • To establish a broad perspective over the foundations of MT, including knowledge-based models.
  • 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):

  • Decoding and heuristic search approaches
  • Minimum Bayes Risk models
  • Word graphs and finite-state models
  • Compilation
  • Confidence measures

Text book:

  • Readings and cases

Reference books/materials:

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

Grading Scheme:

  • 35% Class participation
  • 65% Project

Registration requirement:

  • Instructor's consent required.

Background needed:

  • Background equivalent to COMP526 (Natural Language Processing).
  • Background equivalent to COMP621H (Machine Translation).
  • Background in any AI or statistical areas will help you get more out of the class.

Available for final year UG students to enroll: No.

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: Database

Course description:

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 address these challenges.

Course objective:

This course does not serve as a complete survey of the field; rather, the students are expected to (1) identify a few (existing or new) database problems related to computer networks, (2) describe some (existing or new) solutions to these problems, and (3) demonstrate their (new) contributions in answering a couple of research questions.

Course outline/content (by major topics):

  • Adaptive Query Processing
  • Database-Backed Web Sites
  • Data Streams
  • Internet Search Engines
  • Pervasive Computing
  • Peer-to-Peer Systems
  • Wireless/Embedded DBMS
  • XML Query Processing

Reference books/materials:

  • Ramakrishnan, Raghu et al.: Database Management Systems, 3rd Edition. McGraw-Hill, 2002.
  • Kurose, James F. et al.: Computer Networking: A Top-Down Approach Featuring the Internet. Addison Wesley, 2001.
  • A collection of recent research papers.

Grading Scheme:

Oral presentations, course projects, and writing assignments.

Background needed:

COMP231 and/or COMP530 are preferred background

Available for final year UG students to enroll: YES

Minimum CGA required for UG students: Upon permission of the instructor

Course code: COMP641H
Course title: Topics in Graphics: Computer Vision & Image-based Rendering 3D Computer Vision

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

Area in which course can be counted: Vision & Graphics

Course description:

This course is to introduce the fundamentals of a modern approach to three-dimensional computer vision.

The goal of 3D computer vision is to obtain 3D information from 2D images. The approach is based on the projective geometry as its foundation and on the statistical tools for the automatic computation. Camera calibration, single view geometry, two-view geometry and stereo, multiple view geometry, self-calibration, and image-based rendering will be discussed.

Course objective:

  • fundamental 3D computer vision concepts,
  • fundamental geometric concepts,
  • and basic 3D modelling from images.

Course outline/content (by major topics):

  • Introduction to projective geometry,
  • Introduction to robust statistics.
  • Single view geometry
  • Two-view geometry and stereo
  • Multi-view geometry
  • Self-calibration
  • 3D reconstruction
  • Image-based modeling and 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-

Course Code: COMP660I
Course Title: Topics in Computer and Communication Networks: Pervasive Computing

Instructor: Professor Lionel M. Ni
Room: 3531
Telephone: 2358-7009
Email:
WWW page: http://cse.hkust.edu.hk/~ni/

Area in which course can be counted: Networking & Computer Systems

Course description:

Rapid advances in digital electronics have made computers faster, cheaper, and smaller. Similar progress in communications technology has provided users virtually unlimited bandwidth, anywhere and at any time. The resulting combination of virtually free computation and ubiquitous network access has fueled the new domain of pervasive computing. Pervasive computing envisions environments richly lathered with computation, communication and networked devices, mobile users interacting with their environment using speech and vision, with secure access to personal or public data.

Pervasive computing environments will not simply be stand-alone vehicles for number crunching, rather they will immerse their users in a triad of invisible computation, communication and devices, working in concert to satisfy user requirements according to the facilities available in the environment.

Course objective:

This course will study the mechanisms and environments of pervasive computing. This course will cover many of the maturing technologies in input/output, networking, information infrasture, and ease-of-use that will become necessary as computers become small, pervasive, and in constant connection with each other. Some of the I/O interfaces that will be investigated include speech, vision, gestures, combinations of sensors, and location sensors.

Course outline/content (by major topics):

Topics include computer and network architectures for pervasive computing, mobile computing mechanisms, human-computer interaction, pervasive software systems, location mechanisms, practical techniques for security and user-authentication, and experimental pervasive computing systems.

Reference books/materials:

  • Collection of research papers

Grading Scheme:

We will meet twice a week for a mixture of lectures and class discussions of assigned readings.

Grades will be based on class participation and a course project. Each student will present one or more assigned papers and lead a class discussion.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course Code: COMP670N
Course Title: Topics in TH: String and Tree Algorithms

Instructor: Professor Derick Wood
Room: 3555
Telephone: 2358-6988
Email:
WWW page: http://cse.hkust.edu.hk/~dwood/

Area in which course can be counted: Theoretical CS

Course description:

We will discuss a number of basic algorithms for string matching and similarity, for tree matching and similarity, and for related algorithmic issues.

Course outline/content (by major topics):

String matching; tree similarity

Text book:

  • Graham A Stephen, String Searching Algorithms, World Scientific, 1994

Grading Scheme:

Projects and presentations

Background needed:

UG CS 2nd year at HKUST

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: B+

Course Code: COMP685B
Course Title: Topics in Applications of Computer Science: Computer Music

Instructor: Dr Andrew Horner
Room: 3537
Telephone: 2358-6998
Email:
WWW page: http://cse.hkust.edu.hk/~horner/comp685b/

Area in which course can be counted: Software & Applications

Course description:

The technology of computer music. Music representation, music theory, musical acoustics, spectral analysis, sound synthesis, sound modification techniques and effects, MIDI, musical instrument recognition, pitch detection, signal separation, music on mobile phones. No previous musical background required.

Course objective:

To learn the techniques for synthesizing music in software synthesis, soundcards, hardware synthesizers, and mobile phones.

Course outline/content (by major topics):

Music representation, music theory, musical acoustics, spectral analysis, sound synthesis, sound modification techniques, effects, MIDI, musical instrument recognition, pitch detection, signal separation, music on mobile phones.

Reference books/materials:

  • Computer Music Tutorial by Curtis Roads, MIT Press, 1996
  • Computer Music by Charles Dodge and Thomas Jerse, Schirmer Books, 1997 (2nd Ed)
  • Cooking with Csound by Horner & Ayers, A-R Editions, 2002

Grading Scheme:

  • 2 Hands-on-experience assignments 20%
  • Midterm 40%
  • Project and Presentation 40%

Pre-requisites/Background needed: None

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: Must get permission of instructor, and comp342 is recommended for UG students before they take it.

                                    Class                               Building/  --- Total ----  ----- By School/Dept ----
             Course                 Type  Sect        Schedule            Room     Quot Enrl Resv  Dept/Schl* Quot Enrl Resv        Lecturer/Instructor
----------------------------------- ----- ---- ----------------------- ----------- ---- ---- ----  -------------------------  ------------------------------
COMP512   Distributed OO Sofe Sys     L    1   2FH (Tue 10:30 - 11:50)       1403    40    0    0   Free        40    0    0  CHEUNG, Shing Chi
                                               4FH (Thu 10:30 - 11:50)       1403

COMP537   Knowledge Discovery In DB   L    1   2RT (Tue 16:30 - 17:50)       3006    40    0    0   Free        40    0    0  YANG, Qiang
                                               4RT (Thu 16:30 - 17:50)       3006

COMP621I  Adv. Machine Learning       L    1   1CE (Mon 09:00 - 10:20)       1505    40    0    0   Free        40    0    0  YEUNG, Dit Yan
                                               3CE (Wed 09:00 - 10:20)       1505

COMP621J  Stat. Machine Translation   L    1   5EJ (Fri 10:00 - 12:50)       2578    40    0    0   Free        40    0    0  WU, Dekai

COMP630F  Databases Meet Networks     L    1   1MO (Mon 14:00 - 15:20)       1402    40    0    0   Free        40    0    0  LUO, Qiong
                                               3MO (Wed 14:00 - 15:20)       1402

COMP641H  Computer Vision             L    1   2CE (Tue 09:00 - 10:20)       1505    40    0    0   Free        40    0    0  QUAN, Long
                                               4CE (Thu 09:00 - 10:20)       1505

COMP660I  Pervasive Computing         L    1   2OQ (Tue 15:00 - 16:20)       1511    40    0    0   Free        40    0    0  NI, Lionel M S
                                               4OQ (Thu 15:00 - 16:20)       1511

COMP670N  String & Tree Algorithms    L    1   1FH (Mon 10:30 - 11:50)       4503    40    0    0   Free        40    0    0  WOOD, Derick
                                               3FH (Wed 10:30 - 11:50)       4503

COMP685B  Computer Music              L    1   6DI (Sat 09:30 - 12:20)       LTF     40    0    0   Free        40    0    0  HORNER, Andrew B

COMP690   CS Seminar I                T    1   1QR (Mon 16:00 - 16:50)       LTF    100    0    0   Free       100    0    0  Faculty members of COMP

COMP691   CS Seminar II               T    1   1QR (Mon 16:00 - 16:50)       TBA0   100    0    0   Free       100    0    0  Faculty members of COMP
                                               Remarks : *  Venue: LTF

COMP698   MSc Research Project        LA   1   000 (TBA 00:00 - 00:00)       TBA0    50    0    0   Free        50    0    0  Faculty members of COMP

COMP699   MPhil Thesis Research       **   **  000 (TBA 00:00 - 00:00)       TBA0    50    0    0   Free        50    0    0  Faculty members of COMP

COMP799   Doctoral Thesis Research    **   **  000 (TBA 00:00 - 00:00)       TBA0    50    0    0   Free        50    0    0  Faculty members of COMP

Archive of past courses

This web page was created by Lau Wai Kay on 29 December 2003.

Last modified on 13 January 2004.