Fall 2004 CS Course Listings

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

Archive of past courses

Course Code: COMP530
Course Title: Database Architecture and Implementation

Instructor: Professor Hongjun Lu
Room: 3543
Telephone: x8773
Email:

Area in which course can be counted: Database

Course Description

  • This course introduces basic concepts and implementation techniques in database management systems: disk and memory management; advanced access methods; implementation of relational operators; query processing and optimization; concurrency control and recovery. In addition, a few types of advanced RDBMS applications are covered.

Course Objective

  • This course is a systems-oriented introductory database class for graduate students. The students are expected to learn basic concepts and implementation techniques of relational databases as well as 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
  • Disk and memory management
  • Access methods and indexing
  • Implementation of relational operators
  • Query processing and optimization
  • Concurrency control and recovery
  • Logical and physical database design

Course Organization

  • This course is organized around regular lectures and project assignments. The instructor gives lectures on textbook materials and the TA leads lab sessions about project assignments. The students are expected to finish project assignments individually or in groups. There are midterm and final exams..

Textbook

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

Suggested Background

  • There is no prerequisite for this class. The students are expected to be comfortable with C++ and Java programming. If you are uncertain about your background, come to talk to the instructor during the first weeks.

Grading Policy

  • Project assignments 50%, midterm 20%, final 25%, and class participation 5%.

Course Code: COMP561
Course Title: Computer Networks

Instructor: Jogesh K. Muppala
Room: 3510
Telephone: x6978
Email:
WWW page: http://cse.hkust.edu.hk/~muppala/comp561/

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

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 Third Edition, Addison Wesley, 2004.

Reference books/materials:

  • Larry L. Peterson and Bruce S. Davie, Computer Networks: A Systems Approach, Third Edition, Morgan Kaufmann Publishers, 2003
  • 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 COMP362).

Minimum CGA required for UG students: Permission of the instructor

Course Code: COMP572
Course Title: Introduction to Combinatorial Optimization

Instructor: Mordecai Golin
Room: 3559
Telephone: x6993
Email:
WWW page: http://cse.hkust.edu.hk/~golin/

Area in which course can be counted: Theory

Course description:

  • An introduction to the basic tools of Combinatorial Optimization. Includes: Network flow and the Max-Flow Min cut Theorem, Linear Programming, Matching, Spanning Trees and Matroids Dynamic Programming and basic Graph Algorithms

Course objective:

  • Upon completion of this course students will have been introduced to many of the most basic tools of combinatorial optimization and will be able to apply them towards designing efiicient algorithms in their own research domains.

Textbook:

  • Combinatorial Optimization : Algorithms and Complexity Christos H. Papadimitriou and Kenneth Steiglitz, Dover books, 1998

Pre-requisites/Background needed:

  • Background: COMP271 or equivalent. Linear Algebra

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Only with permission of the instructor

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

Instructor: James Kwok
Room: 3519
Telephone: x7013
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 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 Bayesian methods, kernel methods, manifold learning, and metric/kernel learning.

Reference books/materials:

  • Many recent research papers

Pre-requisites/Background needed:

  • Background in AI, machine learning or pattern recognition

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: Permission of the instructor

Course Code: COMP621K
Course Title: Advanced Topics in AI: Knowledge Representation, Reasoning and Discovery

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:

  • An advanced introduction to logic-based Artificial Intelligence, in particular, the principles of knowledge representation, reasoning, and discovery.

Course objective:

  • To prepare a student to do research in AI. Will also be helpful to students interested in formal methods of software engineering, semantic webs, intelligent agents, data mining, and information integration.

Course outline/content (by major topics):

  • 50% lecture and 50% student presentation
  • Introduction to logic (propositional and first-order).
  • Logic programming with answer set semantics.
  • Reasoning about action in the situation calculus.
  • Learning and inductive logic programming
  • Knowledge discovery in generic domains.

Grading Scheme:

  • One midterm, one project, and one student presentation. No final exam.

Pre-requisites/Background needed:

  • Background in AI

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course Code: COMP630I
Course Title: Topics in DB: Internet and Mobile Information Retrieval

Instructor: Dik Lun Lee
Room: 3534
Telephone: x7017
Email:
WWW page: http://cse.hkust.edu.hk/~dlee/630/

Area in which course can be counted: DB

Course description:

  • Data management on internet: data extraction, integration, indexing, searching, clustering, and peer-to-peer; data management on wireless networks: indexing, caching, scheduling and broadcasting of data on wireless channels, modeling, indexing and querying in location-based information systems.

Course objective:

  • At the end of this course, the students should have acquired:
  • Broad knowledge in data management issues on internet and wireless networks
  • Develop in-depth knowledge in a specific topic by carrying out a course project

Course outline/content (by major topics):

  • Introduction to Information Retrieval
  • Introduction to Wireless Data Access and Dissemination
  • Information Retrieval on the Web
  • Search Engine Log Analysis
  • Peer-to-Peer Search
  • Caching in Wireless Data
  • Indexing Broadcast Data
  • Location-dependent Information services

Grading Scheme:

  • Class participation 10%
  • Presentation 20%
  • Course project 70%

Pre-requisites/Background needed:

  • Background in database management

Available for final year UG students to enroll: No.

Course Code: COMP630J
Course Title: Topics in Spatio-Temporal Databases and Mobile Computing

Instructor: Dimitris Papadias
Telephone: x6971
Email:
WWW page: http://cse.hkust.edu.hk/~dimitris/

Area in which course can be counted: Database

Course objective:

  • To introduce PG students to the state-of the art research on spatio-temporal databases and related topics.

Course outline/content (by major topics):

  • Spatial databases (for static objects)
    • Indexing techniques (mostly focusing on R-trees).
    • Selectivity estimation (cost models, histograms)
    • Spatial query processing in the Euclidean space (selection queries, nearest neighbors, joins).
    • Spatial query processing for road networks and obstacles.
  • Historical spatio-temporal databases
    • Overlapping and multiversion data structures.
    • Spatio-temporal aggregation techniques
  • Predictive spatio-temporal databases
    • Time-parameterized trees
    • Predictive estimation
    • Indexing non-linearly moving objects
  • Spatio-temporal streams
    • Main-memory indexing shemes
    • Approximation techniques
    • Continuous query processing
  • Mobile computing issues
    • Dissemination of location-data.
    • Continuous monitoring
  • Alternative forms of spatial/spatio-temporal query processing
    • Reverse nearest neighbors
    • High dimensional spaces and the curse of dimensionality
    • Clustering and k-medoid problems

Reference books/materials:

  • There will be no textbook or reference book. The course material will be based mostly on recent SIGMOD, VLDB and ICDE papers.

Grading Scheme:

  • Student presentations: 25%
  • Project implementation: 25%
  • Suervey paper on selected topic: 25%
  • Participation and activity in class: 25%

Pre-requisites/Background needed:

  • Background in Databases helpful but not required.

Available for final year UG students to enroll: No

Course code: COMP641I
Course title: Topics in Graphics: Visualization

Instructor: Dr. Huamin Qu
Email:

Area in which course can be counted: Vision & Graphics

Course description:

  • Visualization is a method of generating visual representations of complex multi-dimensional datasets using computer graphics and imaging techniques. This course aims to provide graduate students with an overview of visualization and a survey of the latest advances in this rapidly expanding field. Core visualization and computer graphics techniques and important scientific and commercial applications will be covered. Topics include visual perception and color, 2D visualization, surface and volume visualization, vector-field visualization, visualization systems, and visualization of scientific, medical, engineering, business, and web datasets.

Course objective:

  • To provide a broad overview of visualization; to survey the current hot research topics; to explore new visualization techniques and applications.

Course outline/content (by major topics):

  • The visualization process
  • Human perception
  • Graphics and imaging techniques
  • Scientific visualization
  • Information visualization
  • Visualization systems
  • Case studies

Text book:

  • lecture notes and research papers

Reference books/materials:

  • The Visualization Toolkit: An Object Oriented Approach to 3D Graphics 3rd Edition by W. Schroeder, K. Martin, and B. Lorensen, Kitware, Inc., 2003
  • The Visual Display of Quantitative Information 2nd edition by E. Tufte, Graphics Press, 2001.

Grading scheme:

  • Lab Assignments (30%)
  • Class Presentation (20%)
  • Final project (50%)

Background needed:

  • Basic computer graphics background equivalent to COMP 341

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: permission of the instructor

Course code: COMP696J
Course title: Independent Studies: Pervasive Query Processing

Instructor: Dr. Qiong Luo
Telephone: x6995
Email:

Course description:

  • Pervasive Computing and sensor networks bring many interesting problems to database query processing. In these environments, data are not physically stored on disks any more, but rather are flowing over a network of small, heterogenous devices. Consequently, query processing in these environments must consider device characteristics, communication channels, as well as application requirements. This course introduces necessary background and studies the state of the art of query processing in pervasive computing and sensor networks.

Course outline/content (by major topics):

  • Introduction to pervasive computing and sensor networks;
  • Query processing in sensor networks;
  • Data management in pervasive computing;
  • Embedded and mobile databases;
  • Development environments for handheld devices (J2ME and Microsoft Windows CE .NET).

Reference books/materials:

  • A collection of papers and web sites

Grading Scheme:

  • 30% paper presentation, 20% class discussion, and 50% course project.

Pre-requisites/Background needed:

  • Permission of the instructor

Available for final year UG students to enroll: No.

Course Code  Course Name                                          Section  Schedule           Venue       Instructor
-----------  ---------------------------------------------------  -------  -----------------  ----------  -----------------------
COMP530      Database Architecture and Implementation             L 1      Mon (14:30-15:50)  1511 (42)   PAPADIAS, Dimitrios
                                                                           Wed (14:30-15:50)  1511 (42)
                                                                              
COMP561      Computer Networks                                    L 1      Tue (10:30-11:50)  1511 (42)   MUPPALA, K R Jogesh
                                                                           Thu (10:30-11:50)  1511 (42)
                                                                           
COMP572      Introduction to Combinatorial Optimization           L 1      Tue (12:00-13:20)  1401 (62)   GOLIN, Mordecai J
                                                                           Thu (12:00-13:20)  1401 (62)
                                                                           
COMP621I     Advanced Topics in Machine Learning                  L 1      Tue (09:00-10:20)  1505 (61)   KWOK, James T Y
                                                                           Thu (09:00-10:20)  1505 (61)
                                                                           
COMP621K     Knowledge Representations, Reasoning and Discovery   L 1      Mon (11:00-12:20)  3598 (42)   LIN, Fangzhen
                                                                           Wed (11:00-12:20)  3598 (42)
                                                                           
COMP630I     Internet and Mobile Information Retrieval            L 1      Tue (15:00-16:20)  1402 (76)   LEE, Dik Lun
                                                                           Thu (15:00-16:20)  1402 (76)
                                                                           
COMP630J     Spatio-temporal Databases and Mobile Computing       L 1      Mon (14:30-15:50)  1511 (42)   PAPADIAS, Dimitrios
                                                                           Wed (14:30-15:50)  1511 (42)
                                                                           
COMP641I     Visualization                                        L 1      Tue (16:30-17:50)  1401 (62)   QU, Huamin
                                                                           Thu (16:30-17:50)  1401 (62)
                                                                           
COMP690      Computer Science Seminar I                           T 1      Mon (16:00-16:50)  LTF (134)   Faculty members of COMP

COMP691      Computer Science Seminar II                          T 1      Mon (16:00-16:50)  LTF (134)   Faculty members of COMP

COMP696J     Independent Studies: Pervasive Query Processing      T 1      TBA (00:00-00:00)  TBA         LUO, Qiong
                Remarks :  * Instructor's permission is required for students to enroll into the course.

COMP698      MSc Research Project                                 LA 1     TBA (00:00-00:00)  TBA         Faculty members of COMP

COMP699      MPhil Thesis Research                                ** **    TBA (00:00-00:00)  TBA         Faculty members of COMP

COMP799      Doctoral Thesis Research                             ** **    TBA (00:00-00:00)  TBA         Faculty members of COMP

Archive of past courses

This web page was created by Lau Wai Kay on 8 June 2004.

Last modified on 4 Sept 2004.