Spring 2009 CS Course Listings

This file contains the Spring 2009 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP521 (Cancelled)
Course title: Advanced Artificial Intelligence
Instructor: Fangzhen Lin
Room: 3511
Telephone: 2358 6975
Email:
WWW page: http://cse.hkust.edu.hk/~flin

Area in which course can be counted: AI

Course description (can be more detailed than the one in the calendar):
This advanced AI course will cover the main concepts and techniques in AI. The major topics will be: problem solving, knowledge and reasoning, planning, uncertain knowledge and reasoning, learning, and robotics

Course objective:
Students are expected to gain deep understanding of key concepts and techniques in AI, including heuristic search strategies for single agent problem solving as well as multi-agent strategic planning such as in game playing, knowledge representation and reasoning using both logic and probabilities, machine learning, and integrated agent design.

Course outline/content (by major topics):
1.Introduction.
2. Problem-solving.
3. Knowledge and Reasoning.
4. Planning.
5. Uncertain knowledge and reasoning.
6. Learning.
7. Communicating, perceiving, and acting

Text book:
Stuart Russell and Peter Norvig. Artificial Intelligence - A Modern Approach. Prentice Hall, 2003

Reference books/materials:

Grading scheme: TBA

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP537
Course title: Knowledge Discovery in Databases
Instructor: Raymond Chi-Wing Wong
Room: 3542
Telephone: 2358 6982
Email:
WWW page: http://cse.hkust.edu.hk/~raywong/

Area in which course can be counted: DB/AI

Course description (can be more detailed than the one in the calendar):
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, the field of data mining integrates techniques from database, statistics and artificial intelligence. 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 learn the techniques used in data mining research. To help the students get ready for research.

Course outline/content (by major topics):
1. Association
2. Clustering
3. Classification
4. Data Warehouse
5. Data Mining over Data Streams
6. Web Databases
7. Multi-criteria Decision Making

Text book:
Data Mining: Concepts and Techniques. Jiawei Han and Micheline Kamber. Morgan Kaufmann Publishers (2nd edition)

Reference books/materials:
Introduction to Data Mining. Pang-Ning Tan, Michael Steinbach, Vipin Kumar Boston : Pearson Addison Wesley (2006)

Grading scheme:
Assignment 30%
Presentation 30%
Final Exam 40%

Available for final year UG students to enroll: permission of instructor

Minimum CGA required for UG students: none

Background required: COMP231

Course code: COMP562
Course title: Advanced Computer Communications and Networking
Instructor: Qian Zhang
Room: 3533
Telephone: 2358 8766
Email:
WWW page: http://cse.hkust.edu.hk/~qianzh/

Area in which course can be counted: Networking

Course description (can be more detailed than the one in the calendar):
Advanced principles in computer and communication networking. Multicast routing in the Internet, Peer-to-peer networking; Wireless and mobile networking, Multimedia networking and quality of service, Introduction to network security, Advanced Congestion control in future computer networks.

Course objective:
Students taking this course will have a comprehensive training in all advanced and current aspects of computer networking. They will gain a thorough understanding of the theoretical issues, they will understand the basic principles behind some design choices and the will gain experience of some practical systems. They will understand the current evolution of the Internet and the future trends in the development of the field of networking, which will equip them with the necessary background to start their research in any area of networking.

Course outline/content (by major topics):
1- Introduction to the basic principles of computer networking
2- Broadcast and multicast in computer networks
3- Peer to peer networking
4- Wireless and mobile networking
5- Multimedia networking and Quality of Service in wired and wireless networks
6- Congestion control in high speed and wireless networks
7- Switches and routers architectures and optical networking
8- Security in Computer Networks

Text book:
Kurose and Ross Computer Networking a top down approach (4th Edition)

Reference books/materials:
Tutorials and survey papers from the research literature
Notes and handouts prepared by the Instructors

Grading scheme:
Homework assignments 20% (or 30% if the class is too large)
Classroom presentation 10% (or 0% if the class is too large)
Mid-term exam 30%
Final exam 40%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Pre-requisites: COMP361/ELEC315/COMP561

Course code: COMP573
Course title: Computational Geometry
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 (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 geometric problems. Examples of objects to be studied include Convex hulls, Voronoi diagrams, and Triangulations.

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, 2000.

Grading scheme:
3-5 written assignments 30%
Midterm exam 30%
Final exam 40%

Background needed: COMP271

Available for final year UG students to enroll: Yes.

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

Course ccode: COMP581
Course title: Cryptography and Security
Instructor: Cunsheng Ding
Room: 3518
Telephone: 2358 7021
Email:
WWW page: http://cse.hkust.edu.hk/faculty/cding/COMP581/

Area in which course can be counted: ST

Course description (can be more detailed than the one in the calendar):
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 systems security, and master basic tools for building security systems.

Course outline/content (by major topics):
History of cryptography, classical ciphers, design and analysis of block ciphers and stream ciphers, public-key cryptography, hash functions, digital signature, group signature, proxy signature, user and data authentication, data integrity, nonrepudiation, Key management, public key infrastructure, cryptographic protocols, email security, web security, network security, distributed systems security

Text book: No textbook, but lecture slides will be posted online.

Reference books/materials:
W. Stallings, Cryptography Theory and Network Security, Third/Fourth Edition, Pearson Education, Inc. (ISBN 0-13-091429-0).

Grading scheme: Assignments, midterm and final examination.

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: A-

Background needed: Basic knowledge of computer networks

Exclusion (if applicable): No.

Course code: COMP610H
Course title: Topics in Engineering Enterprise Middleware Platforms
Instructor: Charles Zhang
Room: 3553
Telephone: 2358 6997
Email:
WWW page: http://cse.hkust.edu.hk/~charlesz/comp610/ (for now)

Area in which course can be counted: ST

Course description (can be more detailed than the one in the calendar):
The course will focus on the software engineering aspects of building large-scale and efficient distributed computing systems. Topics of the course include and are not limited to: naming, distributed objects, messaging or event-based systems, stream-oriented systems, container-based architecture, service orientation, resource virtualization, and pattern-oriented server-side design. The course will be conducted as a combination of lectures and reviews of recent research results through paper readings. The course will also feature a series of guest lectures given by industrial experts from global IT companies and financial institutions. The students are to be evaluated based on class participations, paper presentations, and the course project.

Course objective:
Through the course, the students will gain a general understanding of middleware from a software engineering perspective. Students are exposed to various important issues regarding how to enable the effectively development and the deployment of distributed applications. Student will gain empricial experience in building middleware applications and identify useful insights to help with their research objectives.

Course outline/content (by major topics): TBD

Text book: No official textbook.

Reference books/materials:
Distributed Systems, Principles and Paradigms, 2nd Edition, Andrew Tanenbaum and Maarten Van Steen

Grading scheme:
Class participation: 10%
Paper reading: 15%
Paper presentation: 20%
Course project: 55%

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: permission of the instructor

Course code: COMP630M (Cancelled)
Course title: Data Management on New Hardware
Abbreviated Title: Data Mgmt on New Hardware
Instructor: Qiong Luo
Room: 3554
Telephone: 2358 6995
Email:
WWW page: http://cse.hkust.edu.hk/~luo

Area in which course can be counted: DB

Course description (can be more detailed than the one in the calendar):
With the ubiquity of computing equipment, data management issues are prevalent on a wide variety of devices beyond ordinary computers. This course introduces the state of the art on data management on new hardware, such as smart sensor nodes, graphics processors, and handheld devices. The students are expected to carry out research-oriented course projects.

Course objective:
To learn the state of the art on data management on new hardware and to explore research issues in this area.

Course outline/content (by major topics):
database architectures on multicore processors;
databases on graphics processors;
data management on handheld devices;
query processing in sensor networks;
access methods for new storage systems.

Text book: None.

Reference books/materials:
Database Management Systems. Raghu Ramakrishnan and Johannes Gehrke.

Grading scheme:
Class participation (Paper presentation + discussion) 30%, writing assignments (30%), and course project 40%.

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: permission of the instructor

Course code: COMP630O (Cancelled)
Course title: Topics in Database Systems: Mobile Search Engines
Instructor: Dik Lun Lee
Room: 3534
Telephone: 2358 7017
Email:
WWW page: http://cse.hkust.edu.hk/~dlee/630/

Area in which course can be counted: DB

Course description (can be more detailed than the one in the calendar):
Topics in data dissemination and access on internet and wireless networks, including data extraction, searching, clustering, interface, and user profiling and tracking.

Course objective:
1. Acquire broad knowledge in data management issues on internet and wireless networks
2. Develop indepth knowledge in specific topics by carrying out a course project

Course outline/content (by major topics):
1. Course Overview
2. Information Retrieval and Search Techniques
3. Technologies and Performance Concerns in Mobile Data Management
4. Search Engine Personalization, Log Analysis and Location-Based Search
5. Location Modeling and Location Identification
6. Interface, Content Extraction and Summarization for Mobile Information Retrieval
7. Location Tracking, Task and Routine Discovery in Reality Mining
8. Online and mobile communities for Collaborative Filtering

Text book:
None

Reference books/materials:
Papers from the literature

Grading scheme:
Class participation: 15%
Presentation: 20%
Homework assignments: 15%
Course project: 50%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Exclusion: COMP630I

Background needed: COMP231

Course code: COMP630P
Course title: Financial Time Series Data Analysis
Instructor: Lei Chen
Room: 3546
Telephone: 2358 6980
Email:
WWW page: http://cse.hkust.edu.hk/~leichen

Area in which course can be counted: DB

Course description (can be more detailed than the one in the calendar):
Basic idea of time series analysis in both the time and frequency domains. Topics include: Linear time series models, Volatility modeling, Nonlinear models, High-frequency data analysis, Continuous-time diffusion models, and Value at Risk, etc. Real life examples will be used throughout the course.

Course objective:
To learn some basic knowledge of financial time series data, including high-frequency data To study simple models and methods for analysis of financial time series (both for mean and volatility evolution) To assess market risk and to study methods for calculating Value at Risk (VaR)

Course outline/content (by major topics):
1. Returns and their empirical characteristics
2. Linear time series models and their applications
3. Volatility modeling via conditional heteroscedastic models
4. Nonlinear models, neural networks and their applications
5. High-frequency data analysis, realized volatility, and market microstructure
6. Continuous-time diffusion models and Ito's Lemma
7. Value at Risk (VaR), stress test, peak over the threshold, expected loss, and quantiles.
8. Multivariate models, factor models, and their applications

Text book:
Analysis of Financial Time Series by Ruey S. Tsay (John Wiley, 2005), 2nd Ed., ISBN 0-471-69074-0.

Reference books/materials:
* Hamilton, J.D. (1994). Time Series Analysis, Princeton University Press, New Jersey.
* Mills, T.C. (1999). The Econometric Modelling of Financial Time Series, Second Edition, Cambridge University Press, New York.
* Lutkepohl, Helmut (2005). New Introduction to Multiple Time Series Analysis. Springer, Heidelberg.

Grading scheme:
Presentation 20%
Survey Paper 20%
Project 60%

Available for final year UG students to enroll: NO

Background needed: Knowledge in Database and Artificial Intelligence

Course code: COMP696A
Course title: Independent Studies: Statistical Machine Learning
Abbreviated title: Machine Learning
No. of credit: 3
Instructor: Nevin Zhang
Room: 3504
Telephone: 2358 7015
Email:
WWW page: http://cse.hkust.edu.hk/~lzhang

Quota: 5 (instructor's approval is needed for registration)

Description:
In this course, the students are expected to systematically read about the basics of statistical machine learning, as well as recent advances such as non-parametric Bayesian models.

Text book: The course will be based on recent research papers.

Reference books/materials: The course will be based on recent research
papers.

Grading scheme: Oral presentation.

Course code: COMP696B
Course title: Digital Geometry Processing
Abbreviated title: Digital Geometry Process
No. of credits: 3
Instructor: Chiew-Lan Tai
Room: 3515
Telephone: 2358 7020
Email:
WWW page: http://cse.hkust.edu.hk/~taicl

Quota: 3 (instructor's approval is needed for registration)

Description:
Advanced topics in digital geometry processing, including mesh deformation, skeleton extraction, surface reconstruction, skinning animation, and shape matching.

Text book: No specific text books, reading materials will be given.

Reference books/materials: The course will be based on recent research papers.

Grading scheme: presentations and projects

Background: Computer Graphics; good Mathematical background

Course code: COMP696C
Course title: Hybrid Methods for Machine Translation
Abbreviated title: Hybrid Methods Mach Trans
No. of credits: 3
Instructor: Dekai Wu
Room: 3539
Telephone: 2358 6989
Email:
WWW page: http://cse.hkust.edu.hk/~dekai

Quota: 5 (instructor's approval is needed)

Description: We explore recent methods for machine translation that combine statistical machine learning and pattern recognition methods with more structured models incorporating syntactic and semantic modeling.

Text book: None.

Reference books/materials: Readings.

Grading scheme: Project.

Background: COMP526

Course Code: COMP697Y
Course Title: Independent Studies Course: Mathematical tools for coding
Abbreviated Title: Math Tools for Coding
No. of credits: 3
Instructor: Cunsheng Ding
Room: 3518
Telephone: 2358 7021
Email:
WWW page: http://cse.hkust.edu.hk/~cding

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

Description:
After completion of this course, students will master the basic mathematical tools used in coding theory and cryptography.

Main Topics:
Exponential sums, combinatorial designs, finite geometry, number theory.

Grading Scheme:
Assessment will be based on students' performance on assignments.

Text book:
No specific text book, but reading materials will be given.

Background:
Good mathematical background. Approval from the instructor is required.

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 12 Feb 2009.