Fall 2003 CS Course Listings

This file contains the Fall 2003 course listings for the computer science department.

Archive of past courses


Course code: COMP520
Course Title: Fundamentals of digital image processing

Instructor: Dr. Albert Chung
Room: Room 3542
Telephone: 2358 8776
Email:
WWW page: http://cse.hkust.edu.hk/~achung

Area in which course can be counted: AI

Course description:
It is a graduate level course in digital image processing, which provides students with a sound background in this field. Topics include image processing and analysis in the spatial and frequency domains, image restoration and compression, image segmentation, morphological image processing, representation and description, and related application areas and some closely related topics.

Course objective:
To let students learn basic graduate level image processing techniques and related application areas.

Course outline/content (by major topics):
This course will cover the fundamentals of digital image processing. Topics include (Tentative) Image processing and analysis in the spatial domain, Image processing and analysis in the frequency domain, Image restoration and compression, Image segmentation, Morphological image processing, Representation and Description, Related application areas and some closely related topics

Text book:
Related chapters in several references,
e.g. Digital Image Processing by Gonzalez and Woods
Digital Image Processing by Castleman
Two-dimensional Signal and Image Processing by Lim

Reference books/materials:
Related journal articles will be distributed in class

Grading Scheme: N/A

Available for final year UG students to enroll: Yes.

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

Course Code: COMP522
Course title: Machine Learning

Instructor: Dr. James Kwok
Room: 3519
Telephone: 2358-7013
Email:
WWW page: http://cse.hkust.edu.hk/~jamesk

Area in which course can be counted: AI

Course description:
The ability to learn is central to both natural and artificial intelligence. Some major machine learning paradigms will be studied in this course, with special emphasis on theoretically justified, quantitative methods that have been used successfully in real-world applications. This course is not only essential for all research students working in artificial intelligence (including computer vision, pattern recognition, robotics, speech and language processing, uncertainty management, etc.), but is also relevant to some other areas, such as database management and information retrieval.

Course outline/content:
Introduction to major learning paradigms and techniques. Basic applied statistics and information theory. Decision trees. Artificial neural networks. Bayesian classification. Kernel methods. Clustering, Density estimation. Feature selection and extraction. Hidden Markov models. Reinforcement learning. Case-based learning. Model selection and various applications.

Text book:
R.O. Duda, P.E. Hart and D.G. Stork. Pattern classification. 2nd ed. Wiley, 2001.

Grading Scheme:
assignments and exams (details to be determined)

Background needed:
probability theory and linear algebra

Available for final year UG students to enroll: yes

Minimum CGA required for UG students: permission of the instructor

Course Code: COMP526
Course Title: Natural Language Processing

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: AI

Course description:
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.

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:
- 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: COMP530
Course Title: Database Architecture and Implementation

Instructor: Dr. 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 (can be more detailed than the one in the calendar):
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.

Course objective:
To learn basic concepts and implementation techniques of relational databases, and to gain hands-on experience in building components of a small DBMS.

Course outline/content (by major topics):
Introduction to the relational model and SQL. System architectures and implementation techniques of database management systems: disk and memory management; access methods; implementation of relational operators; query processing and optimization; transaction management and recovery. Hands on experience with building the components of a small DBMS.

Text book: Database Management Systems, 3rd Edition. Raghu Ramakrishnan and Johannes Gehrke. McGraw Hill, 2002.

Grading Scheme:
project assignments, midterm and final exams, and class participation.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course Code: COMP561
Course title: Computer Networks

Instructor: Dr. Jogesh K. Muppala
Room:3510
Telephone: 2358-6978
Email:
WWW page: http://cse.hkust.edu.hk/~muppala/

Area in which course can be counted: Computer Engineering

Course description:
Principles, design and implementation of Computer Communication Networks; Network architecture and protocols, OSI reference model and TCP/IP networking architecture; Internet Applications and requirements; Transport protocols, TCP and UDP; Network layer protocols, IP, Routing, Multicasting and Broadcasting; local area networks; data link and physical layer issues; TCP congestion control, Quality of Service, Emerging trends in networking

Course objective:
This course is intended to provide a broad-based and in depth coverage of topics in networking to enable postgraduate students to gain sufficient knowledge and appreciate the issues in networking.

Course outline/content (by major topics):
Introduction to Network and Network Architectures
Application Layer Protocols
Transport Layer Protocols and Issues
Network Layer, Internetworking, Routing and Multicasting
Link Layer and Local Area Networks
Multimedia Networking and Quality of Service
Network Security
New Frontiers in Networking

Text book:
James F. Kurose and Keith W. Ross Computer Networks: A Top Down Approach Featuring Internet Second Edition, Addison Wesley, 2003.

Reference books/materials:
Larry L. Peterson and Bruce S. Davie, Computer Networks: A Systems Approach, Second Edition, Morgan Kaufmann Publishers, 2000
W. Richard Stevens, UNIX Network Programming Vol. 1, 2nd ed., Prentice-Hall, 1998.

Grading Scheme:
Midterm Examination 25 points
Final Examination 35 points
Homeworks and Projects 40 points

Available for final year UG students to enroll: Yes (Note exclusion for COMP 362).

Minimum CGA required for UG students: permission of the instructor

Course Code: COMP573
Course Title: Computational Geometry

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

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

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

Text book:
M. de Berg, M. van Kreveld, M. Overmars and O. Schwarzkopf, Computational geometry---algorithms and applications, Springer-Verlag, Second edition, 2000.

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%

Available for final year UG students to enroll: Yes.

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

Course Code: COMP621H
Course Title: Advanced Topics in AI: Machine Translation

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

Area in which course can be counted: AI

Course description:
We are witnessing an explosion of activity in machine translation, or MT. Many top researchers in the US are shifting into MT, especially the statistical learning approaches pioneered by our group during the past decade. Why?

MT is one of the oldest fields in Computer Science, established by pioneers of the field including Turing, von Neumann, and Chomsky. Yet it remains one of the most fascinating open challenges of science and engineering. MT lies at a crucial point in the field of multilingual Natural Language Processing, exposing many of the most difficult puzzles.

Recent advances spurred by statistical learning models represent one of the most sophisticated attacks on this problem in the history of the field. We are now pushing further by combining knowledge-rich models with the machine learning models. Like the field of Natural Language processing itself, this topic is for those who value a broad, wide-ranging perspective but are not afraid of drilling deep at the same time.

Course objective:
- To explore recent advances in applying statistical and machine learning techniques to MT.
- 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):
- MT 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:
- 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

Background needed:
- Instructor's consent required.
- Background in Natural Language Processing is extremely helpful.
- 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: COMP630H
Course title: Topics in DB Systems: Databases Beyond Relational

Instructor: Dr. Wilfred Ng
Room: 3505
Telephone: 2358-6979
Email:
WWW page: http://cse.hkust.edu.hk/~wilfred

Area in which course can be counted: Database

Course description:
This course presents a coverage of topics concerning major post-relational databases. Database research issues will be discussed in the context.

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

Course outline/content (by major topics):
Incomplete Databases
Fuzzy Databases
Object-oriented Databases
Graphical Databases
Nested Databases
Deductive Databases
Temporal Databases
XML Databases

Reference books/materials:
"A Guided Tour of Relational Databases and Beyond" by Mark Levene and George Loizou, Springer Verlag, 1999
"Fundamental of Database Systems" by R. Elmasri and S. Navathe, Addison-Wesley, 2000
"Data on the Web" by S.Abiteboul, P. Buneman and D. Suciu, 2000
"The Design of Relational Databases" by H. Mannila and K-J Raiha, Addison- Wesley, 1992
Database journals such as IEEE TKDE, ACM TODS
and Information Systems Conference Proceedings such as VLDB, SIGMOD, ICDE, PODS, DEXA

Grading Scheme:
No Final Examination
Group Project Assignment (30%)
Individual Research Report (40%)
Mid-term Test (30%)

Background needed:
You need to have fundamental knowledge about database systems equivalent to the content covered in COMP231.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP631C
Course title: Topics in Knowledge Based Systems: Spatial and Spatio-temporal Databases

Instructor: Dr. Dimitris Papadias
Room: 3503
Telephone: 2358-6971
Email:
WWW page: http://cse.hkust.edu.hk/~dimitris/

Area in which course can be counted: Database

Course description:
An introduction to research issues, problems and solutions for Spatial and Spatio-temporal Databases.

Course objective:
To familiarize PG students with the recent research results in Spatial and Spatio-temporal Databases. To prepare students for future research, especially in the area of Databases.

Course outline/content (by major topics):
Introduction to Spatial Databases.
Multi-dimensional access methods with emphasis on the R-trees and their variations.
Nearest Neighbor Queries
Spatial Joins
Selectivity estimation and multi-dimensional histograms
Introduction to Spatio-temporal Databases
Spatio-temporal Access Methods
Future Prediction
Historical Information Retrieval
Spatio-temporal Aggregation Processing
Novel Query Types
Mobility issues in Spatio-temporal Data Management

Text book: No Textbook
The class will be based on recent papers on the course topic.

Reference books/materials:
Mostly papers in SIGMOD, VLDB, ICDE conferences and TODS, TOIS, TKDE and VLDB journals.

Grading Scheme:
25% student presentations
25% projects
25% survey or research paper
25% participation in class (i.e., presence, questions and any form of active participation).

Background needed:
Background in Databases (introductory course) is desirable

Available for final year UG students to enroll: No

Course Code: COMP670K
Course Title: Topics in Theoretical CS: Online Algorithms

Instructor: Dr. Rudolf Fleischer
Room: 3563
Telephone: 8770
Email:
WWW page: http://cse.hkust.edu.hk/~rudolf

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

Course description:
This is a seminar, i.e., most presentations will be given by the students whose task it is to read and summarize a couple of original papers about a certain topic.

Course objective:
In this course, we will learn how to use competitive analysis (similar to amortized analysis) to analyze the performance of important online algorithms. We will also learn how to prepare a talk and give a successful presentation.

Course outline/content (by major topics):
Introduction: The Ski-Rental problem, The List-Access problem
Basics: Amortized analysis; request-answer games; randomized online algorithms
The List-Access problem II; Splay Trees
The Money-Change problem
The Lost-Cow problem
Paging
The $k$-Server problem
Page migration and replication
Scheduling
Graph coloring
Navigation among obstacles in the plane
Bin packing

Reference books/materials: Original papers

Grading Scheme: Presentations and written summaries by the students.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Archive of past courses

This web page was created by Lau Wai Kay on 20 June 2003.