Fall 2005 CS Course Listings

This file contains the Fall 2005 course listings for the Department of Computer Science.

COMP524 Computer Vision
COMP530 Database Architecture and Implementation
COMP561 Computer Networks
COMP581 Cryptography and Security
COMP621L Kernel and Embedding Methods in Machine Learning
COMP621M Structural Statistical Machine Translation
COMP641K Topics in Graphics: Advanced Graphics Programming
COMP670N Topics in TH: String and Tree Algorithms
COMP680F Topics in CE: Wireless Sensor Networks
COMP680G Advanced topics in real-time embedded systems
Timetable

Course Code: COMP524
Course Title: Computer Vision

Instructor: Tang Chi Keung
Room: 3561
Telephone: x8775
Email: cktang
WWW page: https://course.cse.ust.hk/comp524/ (use the CSD username and password to log on)
Area in which course can be counted: Vision and Graphics

Course description:

Same as academic calendar

Course objective:

Same as academic calendar

Course outline/content (by major topics):

Introduction
Image formation
Image filtering
Edge detection
Segmentation
Segmentation II
Projective geometry
Image warping
Motion estimation
Stereo
Tensor voting
Multiview stereo
Light
Recognition

Text book:

Computer Vision : A Modern Approach, D. Forsyth and J. Ponce

Reference books/materials:

Three-Dimensional Computer Vision, O. Faugeras, MIT Press, 1993
Multiple View Geometry in computer vision , R. Hartley and A. Zisserman, Cambridge University Press, 2000
Robot Vision, B.K.P. Horn, MIT Press, 1986
A Guided Tour of Computer Vision, V. S. Nalwa, Addison Wesley, 1993
Machine Perception, R. Nevatia, Prentice-Hall, 1982
Computer Vision, L. G. Shapiro and G. C. Stockman, Prentice-Hall, 2001
Machine Vision, R. Jain, R. Kasturi, and B.G. Schunck, McGraw-Hill, 1995
Computer and Robot Vision vol. 2, R. Haralick and L. Shapiro, Addison-Wesley, 1992
Object Recognition by Computer - The Role of Geometric Constraints, W.E.L. Grimson, MIT Press, 1990
The Eye, the Brain and the Computer, Fischler and Firschein, Addison-Wesley, 1987
Computer Vision, D. Ballard and C. Brown, Prentice-Hall, 1982
Vision, David Marr, Freeman, 1982
Digital Picture Processing, A. Rosenfeld and A. Kak, Academic Press, 1982

Grading Scheme:

Projects: 84%
Homeworks: 4%
Final Exam (oral): 12%

Background needed:

A good working knowledge of C and C++ programming

Linear algebra

Some mathematical sophistication

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course Code: COMP530
Course Title: Database Architecture and Implementation

Instructor: Dimitris Papadias
Room: 3503
Telephone: x6971
Teaching Assistant: TBA
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 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
Logical database design
Disk and memory management
Access methods and indexing
Implementation of relational operators
Query processing and optimization
Concurrency control and recovery
Physical database design
Advanced Topics

Course Organization:

The instructor will teach the majority of the classes. Students will form groups; each group will choose a general database area (e.g., Data Warehouses and OLAP, Data Mining, XML, Stream Processing, Spatial - Spatiotemporal Databases etc) and prepare: (i) a survey paper on the topic (about 20-25 pages), (ii) a presentation (covering a full class, i.e., 1.5 hours) and (iii) an implementation project.

Textbook:

Database System Concepts, 4th Edition. A. Silberschatz, H. Korth, and S. Sudarshan.
Reference Database Management Systems, 3rd Edition. Raghu Ramakrishnan and Johannes Gehrke.

Suggested Background:

There is no prerequisite for this class. The students are expected to be comfortable with programming.

Grading Policy:

Student Presentations 15%
Survey Paper 15%
Project 20%
Midterm 20%
Final 30%.
The exams will be with open books (any book or notes) and will be based on material explicitly covered during the classes.

Available for final year UG students to enroll: No.

Course Code: COMP561
Course Title: Computer Networks

Instructor: Dr. Yunhao Liu
Room:. 3548
Tel. No.: 2358 7019
Email: liu
Course WWW Page: http://cse.hkust.edu.hk/~liu/comp561/index.htm
Area in which course can be counted: Networking & Computer Systems

Description:

We will cover advanced topics in emerging computer networking technologies, including peer-to-peer and grid computing network, high-speed wide area networks and local area networks, wireless and pervasive computing networks, multimedia networking, and network security.
Lecture material will be drawn from the text books, conference proceedings, readings, and other sources. Students are expected to read the material in advance, and participate in discussions, by offering their ideas and observations.

Textbook:

James F. Kurose and Keith W. Ross Computer Networks: A Top Down Approach Featuring Internet, Third Edition, Addison Wesley, 2004. (http://www.aw-bc.com/kurose-ross/)

Reference Books:

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.
In addition, a collection of papers from journals, conference proceedings, and web sites will be read.

Topics:

The following list gives the approximate order that topics will be covered in comp 561, fall of 2005. Changes/additions will be made as the semester progresses. In addition to the textbook, notes and research papers will be used.

Computer Networks and the Internet
Application Layer
Transport Layer
Network Layer and Routing
Link Layer and Local Area Networks
Multimedia Networking
Internet Security
Peer-to-peer and grid computing
Sensor network
Mobile Computing

Grading Policy:

Homework 15 points
Presentation 15 points
Projects 35 points
Final Examination 35 points

Presentation:

I will distribute a list of papers from major conferences and journals. Each project group (consisting of one or two students) will give a 30 minute presentation on one of the papers they select.

Project:

A project will come from a list that I will distribute or one that the student proposes to me. Every student is required to submit a short project proposal (2 pages). Structure the final project report as a research paper: title, abstract, introduction and motivation, related works, approach description, experimental methodology and results, conclusions, and references. The paper should be single space, double column, 10 fonts, and 12 pages. Three copies of the project report will be submitted. I will review one copy. Students will review the other two copies.

NOTES

The instructor reserves the right to modify course policies, the course calendar, and assignment specifications.
Unless otherwise stated, all work submitted by you should be your own. Copying of assignments, help taken or given in debugging programs or sharing of algorithms and results would constitute cheating. If there is any doubt about the appropriateness of your actions, please contact the instructor for explicit clarification. Cheating is an offense and will result in appropriate disciplinary action against those involved.
Make-ups for examinations may be arranged if your absence is caused by documented illness or personal emergency. A written explanation (including supporting documentation) must be submitted to your lecture instructor; if the explanation is acceptable, an alternative to the examination will be arranged. When possible, make-up arrangements must be completed in advance.

Course Code: COMP581
Course Title: Cryptography and Security

Instructor: Cunsheng Ding
Room: 3518
Telephone: 2358 7021
Email: cding
WWW page: http://cse.hkust.edu.hk/faculty/cding/
Area in which course can be counted: Software and Applications

Course description:

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

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):

History of cryptography, and classical cipher systems
Design and analysis of block ciphers and stream ciphers
Public-key cryptography
One-way functions
Hash functions
Digital signature
Group signature
Proxy signature
User and data authentication
Data integrity
Nonrepudiation
Key management and public key infrastructure
Cryptographic protocols
Email security
Web security
Network security
Distributed systems security

Text book:

No textbook. Detailed lecture slides will be provided.

Grading Scheme:

Assignments
2 quizzes

Background needed:

Basics of computer networks

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: `permission of the instructor' Top UGs should take this PG course, rather than COMP364

Course Code: COMP621L
Course Title: Kernel and Embedding Methods in Machine Learning

Instructor: Dit-Yan Yeung
Room: 3541
Telephone: 2358-6977
Email: dyyeung
WWW page: http://cse.hkust.edu.hk/~dyyeung/
Area in which course can be counted: AI

Course description:

Machine learning is playing an increasingly important role in both artificial intelligence and other areas, including speech and language understanding, computer vision, computer graphics, information retrieval, knowledge discovery and data mining, planning, bioinformatics, electronic commerce, and decision support systems. Many machine learning methods involve transforming data from one representation space to another one, often leading to an increase or decrease in the dimensionality so that the problem becomes more tractable in the new representation space. Recent years have seen intense research interest in kernel methods and embedding methods which are heavily based on this view. Besides studying some models and algorithms from the recent development of kernel methods as well as embedding and manifold learning methods, we will also study relevant research issues brought by applications in other areas.

Course objective:

The objective of this advanced topics course is to help research postgraduate students to keep abreast of some latest development 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. Under the guidance of the instructor, students will write a research paper on a selected topic as the term project.

Course outline/content (by major topics):

Major topics include kernel methods, manifold learning and dimensionality reduction, metric learning, semi-supervised learning, and spectral methods.

Reference books/materials:

Many recent research papers

Grading Scheme:

Class participation: 10%
Class presentation: 20%
Term paper: 70%

Pre-requisites/Background needed:

Background in machine learning or pattern recognition (equivalent to COMP522 and COMP527)

Exclusion (if applicable):

COMP621I

Available for final year UG students to enroll: No

Course Code: COMP621M
Course Title: Structural 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: AI

Course description:

Course description (can be more detailed than the one in the calendar): As the accuracy of statistical machine translation models increases, the limits of the common modeling simplifications become more apparent. In particular, the lack of structure in n-gram based models fails to capture long-distance dependencies, impacting both speed and accuracy. In this course, we will explore the increasing body of results in hybrid models that incorporate tree and syntactic structures into statistical machine translation.

Course objective:

To explore advanced techniques in the latest hybrid structural and SMT (statistical machine translation) 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):

Theory of stochastic transduction grammars
Variant models: complexity and linguistic modeling issues
Empirical studies of hybrid structural SMT models
Comparative analysis
Theory and recent empirical results of ITGs (inversion transduction grammars)

Text book:

Readings and cases

Reference books/materials:

The Theory of Parsing, Translation, and Compiling (Volumes 1 &2), by Alfred V. Aho and Jeffrey D. Ullman. (1972)
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:

Class participation: 35%
Project: 65%

Pre-requisites/Background needed:

Instructor's consent required.
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

Minimum CGA required for UG students: N/A

Course Code: COMP641K
Course Title: Topics in Graphics: Advanced Graphics Programming

Instructor: Dr. Chi-Wing Fu
Room: 3562
Telephone: 2358-6991
Email: cwfu
WWW page: http://cse.hkust.edu.hk/~cwfu/
Area in which course can be counted: Vision and Graphics

Course description:

This course is divided into three major parts: The first part talks about the conventional graphics rendering pipeline. The second part talks about the advanced GPU-based programming methods such as the vertex program, the fragment program, Cg, and GPGPU. The last part discusses different rendering techniques.

Course objective:

After completing this course, students will have deeper knowledge in graphics programming, and have hands-on experience in state-of-the-art graphics rendering methods.

Course outline/content (by major topics):

There are three major parts in this course
- Conventional graphics rendering pipeline
  - The rendering pipeline in details
  - Techniques in optimizing the rendering performance
  - Useful rendering techniques such as billboard, etc.
- Advanced GPU-based programming methods
  - Vertex program
  - Fragment program
  - Cg: C for graphics
  - GPGPU - General Purpose GPU
  - Other useful techniques such as cubemap, texture compression, etc.
- Rendering techniques such as
  - Cartoon rendering
  - Image processing on GPU
  - GPU-based image relighting

Reference books/materials:

material: to be distributed in class or course webpage (e.g., papers and various programming references)

Grading Scheme:

Project I: 35%
Project II: 35%
paper presentation: 30%

Background needed:

Basic computer graphics knowledge - C/C++ & OpenGL programming
COMP341 preferred

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: dwood
WWW page: http://cse.hkust.edu.hk/~dwood
Area in which course can be counted: TH

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: COMP680F
Course Title: Topics in CE: Wireless Sensor Networks

Instructor: Professor Lionel Ni
Room: 3531
Telephone: 2358-7009
Email: ni
WWW page: http://cse.hkust.edu.hk/~ni/
Area in which course can be counted: Networking & Computer Systems

Course description:

This is a seminar-style research-oriented course. The course will cover new advances in wireless sensor networks. Students should have sufficient knowledge in computer systems, operating systems, networking, and wireless communications. Students require the instructor's approval before taking the course.

Course objective:

Research on wireless sensor neworks (WSNs) has recently received a great deal of attention world wide. This course will begin with the background and motivation of WSNs. The course will cover from low-level sensor node design to high-level applications. The focus of this course will be on new challenging research issues in this exciting research area.

Grading Scheme:

Based on projects and class presentation and discussion participation

Available for final year UG students to enroll: No

Course Code: COMP680G
Course Title: Advanced topics in real-time embedded systems

Instructor: Dr Zonghua Gu
Room: 3517
Telephone: 2358-7011
Email: zgu
WWW page: http://cse.hkust.edu.hk/~zgu/
Area in which course can be counted: Networking & Computer Systems

Course description:

Real-time embedded (RTE) systems are prevalent in our modern society, ranging from simple microwave ovens controlled by an 8-bit processor, to complex spacecrafts containing thousands of processors. Software development for RTE systems is often more difficult than for desktop applications, due to the presence of resource constraints and non-functional requirements, such as real-time, low-power and fault-tolerance. This course focuses on techniques for modeling, analysis and implementation of RTE system and software. Main topics addressed include real-time scheduling and resource management, real-time programming languages, real-time networks, software architecture for RTE systems, UML, domain-specific modeling languages, verification and validation, etc.

Course objective:

To present state-of-the-art in real-time embedded systems research, and give PG students necessary background knowledge for conducting research work in this area.

Course outline/content (by major topics):