Fall 2008 CS Course Listings

This file contains the Fall 2008 course listings for the Department of Computer Science and Engineering.

• COMP510 Computational Finance
• COMP511 Fundamentals of Software Analysis
• COMP524 Computer Vision
• COMP530 Database Architecture and Implementation
• COMP538 Introduction to Bayesian Networks
• COMP561 Computer Networks
• COMP570 Introduction to Advanced Algorithmic Techniques
• COMP621H Advanced Topics in AI: Machine Translation
• COMP630N Pervasive Information Retrieval and Data Management
• COMP660I Topics in Computer and Communication Networks: Pervasive Computing
• COMP680J Self-Organizing Networks and Systems
• COMP697O Independent Studies: Mobile Information Systems
• COMP697T Independent Studies: Machine Learning Applications
• COMP697U Independent Studies: Geometry Processing and Rendering
• COMP697V Independent Studies: Research Project in Real-Time Systems
• COMP697W Independent Studies: Bayesian Methods for Machine Learning
• COMP697X Independent Studies: RFID Technologies and Applications
• Timetable

Archive of past courses

Course code: COMP510
Course title: Computational Finance
Vector: 3-0-0:3
Instructor: Steven Skiena
Room: 3538
Telephone: 2358-6972
Email:
WWW page: http://cse.hkust.edu.hk/~skiena/

Area in which course can be counted: N/A

Course description:
Modeling and computational techniques to price vanilla and exotic options; option valuation models and their software implementations; arbitrage concepts; simulation techniques and methodologies; asset price modeling and analysis; solutions of Black-Scholes partial differential equation; lattice tree methods; risk-neutral valuation and Monte-Carlo simulation; finite difference methods; variance reduction and numerical techniques; exotic options pricing (shout options, lookback options, chooser options, barrier options, Asian options, etc.); advanced topics on interest rate derivatives, quantos, etc.; computational exercises and software projects based on C++/object-oriented programming.

Course objective:
The financial industry is a tremendous consumer of advanced computing technologies and mathematical modeling techniques, and a primary employer of computer science graduates. In this course, we will introduce the principles of computational finance and financial data analysis.

Course outline/content (by major topics):
Topics include the workings of financial markets, pricing options and related derivatives, arbitrage arguments, hedging and trading strategies, and techniques for financial time series analysis.

Text book: Hull, "Options, Futures, and other Derivatives", Prentice-Hall, seventh edition.

Reference books/materials: Ruey S. Tsay, Analysis of Financial Time Series John Wiley, 2001.

Grading scheme:
* HW/projects: 55%
* Final exam: 35%
* Class participation:10%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of instructor.

Course Code: COMP511
Course Title: Fundamentals of Software Analysis
Vector: 3-0-0:3
Instructor: S.C. Cheung, Charles Zhang
Room: 3543, 3553
Telephone: 2358-7016, 2358-6997
Email: ,
WWW page: http://cse.hkust.edu.hk/~scc/, http://cse.hkust.edu.hk/~charlesz/, http://course.cse.ust.hk/comp511

Area in which course can be counted: Software & Applications

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

Course objective:
The goal of this course is to introduce how various analysis techniques can be used to manage the quality of a software application. Students will acquire fundamental knowledge of program abstraction, features, verification, testing, refactoring, concurrency, reliability, aspect orientation, and fault analysis. The course will also discuss how to carry out the empirical experimentation for program analysis. Wherever applicable, concepts will be complemented by tools developed in academia and industry. This enables students to understand the maturity and limitations of various analysis techniques.

Course outline/content (by major topics):
Program Features, Program Abstraction, Static Analysis, Testing, Concurrency, Empirical Experimentation

Textbook: None

Reference books/materials:
* Paul Ammann and Jeff Offutt, Introduction to Software Testing, Cambridge University Press, 2008.
* Mauro Pezze and Michal Young, Software Testing and Analysis - Process, Principles, and Techniques, 1st edition, John Wiley & Sons, 2008.
* Claes Wohlin et al., Experimentation in Software Engineering, Kluwer Academic Publishers, 2000.
* Jeff Magee and Jeff Kramer, Concurrency - State Models & Java Programming, 2nd edition, John Wiley & Sons, 2006.

Grading scheme:
* Class Participation (10%)
* Assignments (50%)
* Final examination: (40%)

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course Code: COMP524
Course Title: Computer Vision
Vector: 3-0-0:3
Instructor:  C K Tang
Room: 3561
Telephone: 2358-8775
Email:
WWW page:  http://cse.hkust.edu.hk/~cktang/, https://course.cse.ust.hk/comp524/ (use CSD username and password)

Area in which course can be counted: Vision and Graphics

Course description (can be more detailed than the one in the calendar):
See course catalog in the academic calendar.
 
Course objective:
To equip students with the fundamental computer vision knowledge, so that they can kick start research in related areas of their interest.

Course outline/content (by major topics):

• Introduction
• Image formation
• Image filtering
• Edge detection
• Segmentation
• Segmentation II
• Texture
• Projective geometry
• Image warping
• Stereo
• Surface
• Multiview stereo
• Light
• Photometric stereo
• Optical flow
• Recognition

Textbook:
Computer Vision : A Modern Approach, D. Forsyth and J. Ponce
 
Reference books/materials:
1.  Three-Dimensional Computer Vision, O. Faugeras, MIT Press, 1993
2.  Multiple View Geometry in computer vision , R. Hartley and A. Zisserman, Cambridge University Press, 2000
3.  Robot Vision, B.K.P. Horn, MIT Press, 1986
4.  A Guided Tour of Computer Vision, V. S. Nalwa, Addison Wesley, 1993
5.  Machine Perception, R. Nevatia, Prentice-Hall, 1982
6.  Computer Vision, L. G. Shapiro and G. C. Stockman, Prentice-Hall, 2001
7.  Machine Vision, R. Jain, R. Kasturi, and B.G. Schunck, McGraw-Hill, 1995
8.  Computer and Robot Vision vol. 2, R. Haralick and L. Shapiro, Addison-Wesley, 1992
9.  Object Recognition by Computer - The Role of Geometric Constraints, W.E.L. Grimson, MIT Press, 1990
10. The Eye, the Brain and the Computer, Fischler and Firschein,Addison-Wesley, 1987
11. Computer Vision, D. Ballard and C. Brown, Prentice-Hall, 1982
12. Vision, David Marr, Freeman, 1982
13. Digital Picture Processing, A. Rosenfeld and A. Kak, Academic Press, 1982 
 
Grading scheme:
Projects: 64%
Homework: 4%
Final Exam: 32%

Available for final year UG students to enroll: Yes
 
Minimum CGA required for UG students: A- or permission of the instructor

Course Code: COMP538
Course Title: Introduction to Bayesian Networks
Vector: 3-0-0:3
Instructor: Nevin L. Zhang
Room: 3504
Telephone: 2358-7015
Email:
WWW page: http://cse.hkust.edu.hk/~lzhang/

Area in which course can be counted: Artificial Intelligence

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

Bayesian networks (BNs) are a framework for dealing with the complexity that arises when applying probability theory in complex systems. They represent the structure of a system using a directed graph of random variables. Conditional independencies can be readily identified from the graph and are used to drastically reduce the complexity of inference. Model construction can be done manually using the intuitively appealing graphical interface provided. Alternatively, models can be learned from data via statistical principles such as maximum likelihood estimation and Bayesian estimation. The latter is the focus of much recent research and has attracted much attention in the AI, machine learning, and data mining communities.

Course objective:

The course consists of lectures by the instructor (75%) and student presentations (25%). The lectures are designed to provide a solid training in the theory and methods of BNs, while the student presentations are designed to convey an overview of the field.

Course outline/content (by major topics):
     * Unit 0: Preparation
           o Introduction to course
           o Multivariate Probability and Information Theory
     * Unit 1: Basic concepts
           o Bayesian networks
           o D-separation
     * Unit 2: Inference
           o Variable elimination
           o Clique tree propagation
     * Unit 3: Parameter learning
           o Complete data
           o Incomplete data
     * Unit 4: Structure learning
           o Structure Learning:
           o Optimal Structure Learning
           o Learning Latent structures
     * Term project presentations
           o Current trends (literature survey: Statistical machine
             learning, Statistical relational learning, Dynamic BN,
             conditional Markov field, the information bottleneck
             approach, relationships to other probabilistic models/data
             analysis techniques, etc)
           o Applications (literature survey: Bioinformatics, etc )
           o Applications (Mini research project)

Textbook:
    N.L. Zhang and H.P. Guo (2006). Introduction to Bayesian Networks. Science Press, Beijing.

Reference books/materials:

    * Stuart J. Russell (1995). Artificial intelligence: a modern approach. Prentice Hall. Call No. Q335 .R86.
    * Judea Pearl (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers. Call No. Q335 .P383.
    * Jensen, F. V. and Nielsen, T. D. (2007). Bayesian Networks and Decision Graphs (second edition) (http://bndg.cs.aau.dk), Springer.

Grading scheme:

    * Class participation: 10 (Based on impression. So make yourself heard in class.)
    * Final exam: 40 (Required for all PG courses)
    * Project report: 40 (30 content & depth, 5 organization and presentation, 5 English )
    * Oral presentation: 10

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: B+

(can be "permission of the instructor")

Course code: COMP561
Course title: Computer Networks
Vector: 3-0-0:3
Instructor: Qian Zhang
Room: 3533
Telephone: 2358-8766
Email:
WWW page: http://cse.hkust.edu.hk/~qianzh/, http://cse.hkust.edu.hk/~qianzh/COMP561-Fall2008/

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

Course objective:
This course discusses the basic principles of computer networks, the architectures and protocols. It follows a top down approach covering application protocols such as HTTP, FTP, e-mail, P2P file sharing, transport protocols in particular UDP and TCP, the Internet routing and multi-access control protocols at the link layer. We will also cover some advanced topics in emerging computer networking technologies, including wireless and pervasive computing networks, peer-to-peer networks, and network security.

Lecture material will be drawn from the text books and other reading sources. Students are expected to read the material in advance, and participate in discussions, by offering their ideas and observations.

Textbook:
James Kurose and Keith Ross, Computer (http://www.aw-bc.com/kurose-ross/)
Networks: A Top Down Approach, the 4th Edition, Addison Wesley, 2007.

Grading scheme:
* Assignments (30%)
* Paper review (10%)
* Class project report (10%)
* Final examination: (50%)

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course Code: COMP570
Course Title: Introduction to Advanced Algorithmic Techniques
Instructor: Sunil Arya
Room: 3509
Telephone: 2358-8769
Email:
WWW page: http://cse.hkust.edu.hk/~arya/

Area in which course can be counted: Theory

Course description:
Please refer to the course catalog.

Course objective:
To equip students with a broad knowledge of general techniques for designing and analyzing algorithms.

Course outline/content (by major topics):
o Advanced Data Structures as an Algorithmic Design Tool
o Amortization
o Efficient Graph Algorithms
o Approximation Algorithms and Schemes
o Online Algorithms and Competitive Analysis
o Randomized and Probabilistic Analysis

Textbook: None

Reference books/materials:

o Algorithm Design. Jon Kleinberg and Eva Tardos, Addison Wesley, 2005.
o Randomized Algorithms. Rajeev Motwani, Prabhakar Raghavan, Cambridge University Press, 1995.
o Introduction to Algorithms (2nd Edition). T. Cormen, C. Leiserson, R. Rivest, C. Stein. McGraw Hill and MIT Press, 2001.
o Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Mitzenmacher and E. Upfal. Cambridge University Press, 2005.

Grading scheme: Based on homeworks and exams.

Available for final year UG students to enroll: Yes.

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

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.

Textbook:
- 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: COMP630N
Course title: Pervasive Information Retrieval and Data Management
Vector: 3-0-0:3
Instructor: Wang-Chien Lee
Room: 3538
Email:
WWW page: http://www.cse.psu.edu/~wlee/

Area in which course can be counted: Databases

Course description:
This is a graduate level course introducing information retrieval and data management in pervasive computing systems. Topics covered pervasive data management and information concepts and techniques from application, system and networking aspects. The course will introduce basics of pervasive applications, including mobile databases, mobile information retrieval (search engines), and location based services and fundamentals of a number of pervasive computing systems and networks, including mobile cellular networks, wireless data broadcast systems, wireless sensor networks, mobile ad hoc networks, and peer-to-peer systems. Various technical issues (including routing, localization, data accessing, dissemination, indexing, caching, aggregation, query processing, monitoring, ranking, mining, and personalization) involved in supporting information retrieval and data management in pervasive computing systems and networks will be discussed in depth.

Course objective:
1. Obtain broad knowledge and solid background in fundamental concepts and system issues in pervasive information retrieval and data management, from
aspects of computing systems, networks, and applications.
2. Explore research issues and topics in pervasive information retrieval and data management.

Course outline/content (by major topics):
* Introduction to pervasive computing vision and practice
* Introduction to information retrieval and data management
* Introduction to pervasive computing systems and networks
        -      Mobile systems (cellular/client-server); wireless data broadcast systems; wireless sensor networks; mobile ad hoc networks; (mobile) peer-to-peer systems
        -      Localization
        -      Routing
* Introduction to pervasive information retrieval and data management
        -      Network (overlay) infrastructures and distributed index
        -      Web browsing on PDAs
        -      Personalized and mobile search engine
        -      Location based services
        -      Data access and dissemination
        -      Query processing and moving object monitoring
        -      Data caching
        -      Ranking, mining, and personalization issues

Text book: None.

Reference books/materials: Research papers from the literature

Grading scheme:
- Class participation (20%)
- Presentation (15%)
- Term project (65%)

Background needed: database and network background

Available for final year UG students to enroll: Permission by instructor

Minimum CGA required for UG students: N/A

Course code: COMP660I
Course title: Topics in Computer and Communication Networks: Pervasive Computing
Instructor: Yunhao Liu
Room: 3548
Telephone: 2358-7019
Email:
WWW: http://cse.hkust.edu.hk/~liu/

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: COMP680J
Course title: Self-Organizing Networks and Systems
Vector: 3-0-0:3
Instructor: Lionel Ni
Room: 3503
Telephone: 2358-6999
Email:
WWW page: http://cse.hkust.edu.hk/~ni/

Area in which course can be counted: Computer Engineering

Course description:

• In a self-organizing network or system, each node in the system communicates only with its immediate neighbors.
• Neighbors relay messages to their neighbors in turn until the message reaches its destination.
• Such a fully decentralized distributed system has many interesting features and has attracted attention to researchers in the design of large-scale distributed systems.
• Examples of such systems include peer-to-peer networks, wireless sensor networks, and wireless mesh networks.

Course objective:

• This course will study the mechanisms and environments of self-organizing networks and systems.
• Many special features such as node autonomity, power consumption, localized transmission, node mobility, dynmaic routing, self-configuration, and self-healing will be studied.

Course outline/content (by major topics): Topics include peer-to-peer computing, wireless sensor networks and wireless mesh networks.

Textbook: no

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.

Background needed: Permission of the instructor

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP697O
Course title: Independent Studies: Mobile Information Systems
Abbreviated title: Mobile Information Systems
Vector: 3-0-0:3
Instructor: Dik Lun Lee
Room: 3534
Telephone: 2358-7017
Email:
WWW page: http://cse.hkust.edu.hk/~dlee/

Quota: 1 (instructor's approval is needed for taking the course)

Area in which course can be counted: N/A

Course description:
Techniques for designing information systems for mobile wireless environments; data broadcast methods; caching, indexing and query processing; spatial indexes; resource-constrained, mobile and ubiquitous information access

Course outline/content (by major topics):
- Indexing and caching in Data broadcast systems
- Multi-channel and data dependent broadcast
- Open platforms for mobile applications
- Indexing and query processing in spatial databases
- Ubiquitous and pervasive information systems
- Context-awareness, mobility and user models
- User and system interactions in resource-constrained systems

Textbook: Nil.

Reference books/materials: Will be available on-line or distributed in class.

Grading scheme: (T.B.A.)

Pre-requisites/Background needed: Permission of the instructor.

Available for final year UG students to enroll:

Course code: COMP697T
Course title: Independent Studies: Machine Learning Applications
Abbreviated title: Machine Learning
Vector: 3-0-0:3
Course quota: 3 (instructor's approval is needed for registration)
Course instructor: Qiang Yang
Room: 3563
Telephone: 2358-8768
Email:
WWW page: http://cse.hkust.edu.hk/~qyang/

Area in which course can be counted: N/A

Description:
In this course we will survey the field of machine learning applications in several application areas, including information retrievaland wireless sensor networks.

Grading scheme: Final report 100%

Final exam: No

Course code: COMP697U
Course title: Independent Studies: Geometry Processing and Rendering
Abbreviated title: Geometry Processing
Course vector: 3-0-0:3
Instructor: Pedro Sander
Room: 3525
Telephone: 2358-6983
Email:
WWW page: http://cse.hkust.edu.hk/~psander/

Quota: 4 (instructor's approval is needed for taking the course)

Area in which course can be counted: N/A

Course description:
This course will cover recent geometry processing and rendering algorithms. The course will first provide an in depth analysis of latest graphics hardware (GPUs), and then will focus on algorithms that make best use of the technology for efficient processing and rendering of three dimensional models.

Course outline/content (by major topics):
   1. GPU Trends
   2. The Unified Graphics Architecture
   3. Efficient Mesh Processing Algorithms
   4. Efficient Mesh Rendering Algorithms

Text book:
None. Reading material consists of research papers.

Grading scheme:
No exams. Students will be accessed based on their performance on the weekly meetings and paper discussions.

Background needed: Graphics MPhil and PhD students w/ approval of instructor.

Available for final year UG students to enroll: No

Course code: COMP697V
Course title: Independent Studies: Research Project in Real-Time Systems
Abbreviated title: Real-Time Systems
Course vector: 3-0-0:3
Instructor: Zonghua Gu
Room: 3517
Telephone: 2358-7011
Email:
WWW page: http://cse.hkust.edu.hk/~zgu/

Quota: 3 (instructor's approval is needed for taking the course)

Area in which course can be counted: N/A

Course description:
This course offers students experience in doing research work on real-time embedded systems

Course outline/content (by major topics):
1. Real-Time Scheduling.
2. Formal Verification
3. Model-Checking

Textbook: N/A

Grading scheme: Project

Background needed: None

Available for final year UG students to enroll: No

Course code: COMP697W
Course title: Independent Studies: Bayesian Methods for Machine Learning
Abbreviated title: Bayesian ML
Course vector: 3-0-0:3
Instructor: Dit-Yan Yeung
Room: 3541
Telephone: 2358-6977
Email:
WWW page: http://cse.hkust.edu.hk/~dyyeung/

Quota: 1

Area in which course can be counted: N/A

Course description:
The objective of this course is to help research postgraduate students to expand the list of available tools in their machine learning toolbox beyond those learned from an introductory machine learning course. The focus will be on Bayesian methods for machine learning.

Course outline/content (by major topics):
Bayesian inference
Kernel methods
Graphical models
Approximate inference
Sampling methods
Continuous latent variables
Combining models
Semi-supervised learning

Textbook:
Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer.

Grading scheme:
Discussions (20%)
Problem sets (40%)
Programming projects (40%)

Background needed:
Introductory course on machine learning

Available for final year UG students to enroll: no

Course Code: COMP697X
Course Title: Independent Studies: RFID Technologies and Applications
Abbreviated Course Title: RFID Technologies
Course vector: 3-0-0:3
Instructor: Lionel Ni
Room: 3503
Telephone: 2358-6999
Email:
WWW page: http://cse.hkust.edu.hk/~ni/

Area in which course can be counted: N/A

Course description:
RFID is used in enterprise supply chain management to improve the efficiency of inventory tracking and management. In this independent study, students have to learn the state-of-the-art RFID technologies, including active/passive/semi-passive tags, various readers, security issues, system integration, indoor location tracking, some emerging applications, and challenging research issues.

Course outline/content (by major topics):
Topics include RFID tags, Current and Potential Applications, RFID-based location tracking, Security and privacy issues, Regulation and Standardization.

Textbook: no

Grading scheme: Based on meeting discussions and final report.

Background needed: Permission of the instructor

Available for final year UG students to enroll: Yes

Please visit https://www.ab.ust.hk/wcr/cr_class_staf_main.htm for the timetable and quota.

Archive of past courses

Last modified by An Lu on 10 Sep 2008.