Spring 2010 CS Course Listings

This file contains the Spring 2010 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP521
Course title: Advanced Artificial Intelligence
Instructor: Fangzhen Lin
Room: 3511
Telephone: 23586775
Email:
WWW page:

Area in which course can be counted: AI

Course description: This advanced AI course will cover advanced 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:

Course outline/content (by major topics):

Textbooks:

Reference books/materials:

Grading scheme:

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP562
Course title: Advanced Computer Communications and Networking
Instructor: Qian Zhang
Room: 3533
Telephone: 23588766
Email:
WWW page: http://cse.hkust.edu.hk/~qianzh/

Area in which course can be counted: NT

Course description: 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.

Prerequisite: COMP 361 or COMP 561 or ELEC 315

Course outline/content (by major topics): Broadcasting and Multicasting
Peer-to-Peer Networking
Wireless Networking
Multimedia Networking and Quality of Service Provision
Advanced Topics for Congestion Control Network Security

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

Reference books/materials:

Grading scheme: Homework: 30 points
Paper presentation: 15 points
Project report: 15 points
Final Exam: 40 points

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP573
Course title: Computational Geometry
Instructor: Ke Yi
Room: 3552
Telephone: 23588770
Email:
WWW page: http://cse.hkust.edu.hk/~yike/

Area in which course can be counted: Theory

Course description: 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

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

Reference books/materials:

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 the instructor

Course code: 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: COMP621R (Cancelled)
Course title: Automatic Speech Recognition in Practice
Instructor: Brian Mak
Room: 3513
Telephone: 23587012
Email:
WWW page:

Area in which course can be counted: AI

Course description: The course is an introduction to Automatic Speech Recognition (ASR). The various mathematical and engineering tools employed in IBM's standard "recipe" for training large-vocabulary ASR systems will be used as the backbone in the discussion. They include discriminant analysis, acoustic modeling, speaker adaptive training, adaptation, and discriminative training. Alternative technologies will then be explored.

Course objective: To introduce the science and technologies behind state-of-the-art Automatic Speech Recognition (ASR) systems. Students, after taking this course, should be able to build a reasonable ASR system from scratch. Although the various mathematical and engineering tools are taught for ASR, students should be able to apply them to other areas of pattern recognition such as handwriting recognition and time sequence modeling, as well as other areas of AI such as machine translation.

Course outline/content (by major topics): 1. Introduction to automatic speech recognition (ASR)
2. Speech, production and perception
3. Pattern classification, mathematical modelling, and estimation theory
4. Hidden Markov modeling
5. Acoustic modeling
6. Language modeling
7. Discriminant analysis
8. Adaptation and adaptive training
9. Discriminative training
10. Finite state transduction
11. ASR: putting it altogether

Textbooks: Xue-Dong Huang, Alex Acero, and Hsiao-Wuen Hon "Spoken Language Processing", Prentice Hall PTR, 2001.

Reference books/materials: L. Rabiner and B.H. Juang
"Fundamentals of Speech Recognition"
Prentice Hall, 1993.

R.O. Duda, P.E. Hart, and D. G. Stork
"Pattern Classification", 2nd Ed.
Prentice Hall.

Grading scheme: assignments, project, and paper presentation

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP621S
Course title: Advanced Topics in AI: Computational Game Theory and Applications
Instructor: Boi Faltings
Email:
WWW page: http://liawww.epfl.ch/~faltings/

Area in which course can be counted: AI

Course description: Computer networks are often used to mediate complex interactions, including the division of shared resources such as wireless spectrum or advertisement space, joint decisions such as allocation of computation tasks or scheduling meetings, obtaining truthful information in reputation systems, and protecting against intrusions in information security. Game Theory is commonly used to model such interactions, and computational game theory is about computational methods to implement them based on game-theoretic principles.

Course objective: This course will provide an introduction to computational game theory with a particular emphasis on algorithms and methods for specific application scenarios.

Course outline/content (by major topics):
- Normal form games, pure and mixed strategies, Equilibria: Nash, correlated, mediated, conjectural, Stackelberg;
- Computing equilibria (in particular Nash and Stackelberg), applications in wireless spectrum access, airport security, information security
- Coalitions and Negotiation
- Social choice; voting; manipulation
- Mechanism design; incentive-compatibility, VCG mechanisms, online mechanisms, applications in auctions, combinatorial auctions, internet ad auctions, network routing, resource sharing
- Distributed implementations of mechanisms through constraint satisfaction/optimization
- Mechanisms without money: value aggregation, exchanges, matching
- Eliciting truthful information, with applications to rating systems and information markets

Textbooks: none.

Reference books/materials:
Yoav Shoham/Kevin Leyton-Brown: Multiagent Systems
Noam Nissan, Tim Roughgarden, Eva Tardos, Vijay Vazirani: Algorithmic Game Theory
Research papers and materials prepared by the instructor

Grading scheme: Class participation, course project and presentation, final examination

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP621T
Course title: Image Analysis and Processing
Abbreviated Title: Image Analysis and Process Instructor: Dr. Albert Chung
Room: 3516
Telephone: 8776
Email:
WWW page: To be available

Area in which course can be counted: AI

Course description: This course introduces basic computational techniques for image analysis and processing. Topics include image segmentation, image registration and image filtering. Some sophisticated image processing and analysis tools and state-of-the-art may also be introduced subject to the availability of time. Projects, reports and presentations are required.

Course objective: The main objective is to give students a board overview of theory and practical issues of the current and commonly used algorithms in image analysis and processing.

Course outline/content (by major topics):
- Introduction to image analysis and processing
- Image segmentation including statistical-based and contour-based methods
- Image registration including rigid and non-rigid registration methods
- Image filtering including non-linear filtering
- Projects related to Image Analysis and Processing

Textbooks: List of technical papers will be made available on-line. Reference books will be listed in the course website.

Grading scheme: Presentations, Projects and Reports.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP630O
Course title: Topics in Database Systems: Mobile Search Engines
Instructor: Dik Lun Lee
Room: 3534
Telephone: 23587017
Email:
WWW page: http://cse.hkust.edu.hk/~dlee/630/

Area in which course can be counted: DB

Course description: 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

Textbooks: 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: COMP641O
Course title: Topics in Graphics: Image-based Modeling
Instructor: Long QUAN
Room: 3506
Telephone: 23587018
Email:
WWW page:

Area in which course can be counted: VG

Course description: The course consists of three major parts: vision geometry, computation of vision geometry, and object representations. It covers the entire pipeline of obtaining the final objects from pixels. The materials are based on the recent book, 'image-based modeling' by the instructor, available soon in the coming semester.

Course objective:

Course outline/content (by major topics): geometry prerequisite, geometry of multiple views, feature detection, structure from motion, and object modeling.

Textbooks:

Reference books/materials:

Grading scheme: T.B.A

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP660M
Course title: Topics in Computer and Communication Networks: Queuing System and Its Applications
Instructor: Bo LI
Room: 3524
Telephone: 23586976
Email:
WWW page: course.cse.ust.hk/comp660m

Area in which course can be counted: NT

Course description: This is an introductory course to queueing system and its applications in computer systems and communications. Topics include markov process, M/M/1 model, M/G/1 equilibrium and analysis, bulk arrival, open and closed networks with closed-form solutions. It will also discuss research papers with queueing analysis.

Course objective: It teaches students the queueing theory through concrete examples in computer systems and communications.

Course outline/content (by major topics):
* probability and random process
* Common probability distributions
* Markov process and discrete markov chain
* Birth-Death process
* Kendall's notations and Little's result
* M/M/1, M/M/n
* Queues with bulk arrival
* M/G/1, residual time and imbedded markov chain
* Open network, Jackson theorem
* Closed queueing network, convulution algorithms

Textbooks: None

Reference books/materials: will provide later

Grading scheme: Midterm exam and term report

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP660N
Course title: Special Topics in Advanced Storage Systems
Abbreviated title: Advanced Storage Systems
Instructor: Prof David Du
Room: 3538
Telephone: 23586972
Email:
WWW page:

Area in which course can be counted: NT

Course description:
This is a graduate level course. All students are assumed to know the basic concepts of computer networks and operating systems. The emphasis of the class is on how to do research in storage area networks and future storage systems. Although there are many exciting research issues and subjects in these areas, we will focus on the following research subjects: storage area networks, future storage systems, data backup and deduplication, flash memory based Solid State Drives (SSD), long-term data preservation, power management, and data/storage security/privacy.

The Internet today has grown to an enormously large scale. Devices large and small are connected globally from anywhere on the earth. Therefore, we can argue that we are in a network-centric era. With rapid technological advancements, we now also have cheap and small devices with high computing power and large storage capacity. These devices are designed to improve our daily life by monitoring our environment, collecting critical data, and executing special instructions. These devices have gradually become an essential part of our Internet. Many imaging, audio and video data are converted from analog to digital. As a result, unprecedented amount of data are collected by these devices and are available via the Internet. How to manage and look for the desired information becomes a great challenge. Therefore, we can certainly say that we are in a data-centric era. In this course, we examine the challenges in the convergence of both network-centric and data-centric computing. At the same time, many emerging applications like service-oriented, security/privacy and real-time applications demand much better support than the current Internet can offer. To meet these challenges, the current Internet needs to be resigned from scratch. However, how the future Internet should look is still undetermined. Another important aspect is how to cope with the enormously large volume of data that we have collected and are continuously generating. We will examine the essential changes in data representation, information retrieval, storage systems and networking design.

In addition to the traditional goals of designing large-scale storage systems like performance, scalability, availability and reliability, other challenges including manageability, searching for the desired information, energy efficiency, long-term data preservation and data security/privacy become increasingly important for storage systems. We will discuss the evolutionary development path of past storage systems and the impact of new technology like flash memory based solid state drives. The impact of the advancement of disk technology on fault-tolerance will be presented. The potential solutions and research issues of the new challenges will be also covered. These include how to preserve data for longer terms (more than 100 years), data/storage security/privacy issues, data backup and deduplication, data storage virtualization, and power management for data center.

Course objective: This is a graduate level course. The objective are a) getting familiar with the new development and research issues in current and future advanced storage systems, and b) training graduate students for how to carry out research methodology and practicing research experience.

Course outline/content (by major topics):
* Overview of Current Mass Storage Systems
* New Computing Environment and Challenges
* Object Storage Concept
* Storage Area Networks
* Evolution Path of Storage Systems
* Storage Virtualization
* IP-Storage
* Solid State Drives
* Long-Term Data Preservation
* Data Deduplication
* Data Center Power Management
* Privacy and Storage Security
* Data Center and Cloud Storage Systems

Textbooks: None

Reference books/materials: Various papers will be provided

Grading scheme: TBA

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP696G
Course title: Independent Studies: Exponential Families and Probabilistic Graphical Models
Abbreviated title: Graphical Model Topics
Instructor: Prof Nevin Zhang
Room: 3504
Telephone: ext-7015
Email:
Quota: 5 (instructor's approval is needed for registration)
No. of credits: 3

Course description: Advanced topics in probabilistic graphical models (PGMs), including PGMs as an exponential family, variational inference, variational learning, Gaussian networks, Markov networks.

Course objective: In this course the students will learn to view probabilistic graphical models (PGMs) from the perspective of exponential families and thereby gain insights into inference and learning algorithms for PGMs. It is hoped that the students can acquire the necessary mathematics background to work with PGMs.

Mode of operation:The course will meet *once each week in one 3-hour session*. One student will deliver a lecture at each session. The presenter should inform the class of the content at least 3 days before the lecture and all participants are expected to study the materials carefully before class.

Tentative Outline:
Detailed Discussion
* KF, Chapter 8: The exponential family
* WJ: Sections 3.2-3.3
* KF, Chapter 11: Inference as optimization (Variational Inference)
* KF: Chapter 16: Learning Graphical Models: Overview
* KF: Chapter 19: Partially Observed data
* WJ: Selected sections. (Theoretical treatment of variational inference and learning)
Overview Discussion
* KF: Chapter 12: Particle-Based Approximation (Sampling)
* KF: Chapter 14: Inference in Hybrid Networks (Gaussian networks)
* KF: Chapter 20: Learning Undirected Models (Markov networks)

Grading: Students will be graded based on the lectures.

References:
KF: D. Koller and N. Friedman (2009). Probabilistic graphical models: Principles and techniques. The MIT Press.
WJ: M. J. Wainwright and M. I. Jordan (2008). Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1: 1-305.

Minimum CGA required for UG students: permission of the instructor

Course code: COMP696H
Course title: Independent Studies: Semantic Web Science
Abbreviated title: Web Science
Instructor: Helen Shen
Room: 3557
Telephone: 2358-6987
Email:
Quota: 6 (instructor's approval is needed for registration)
No. of credits: 3

Course description: Topics to be covered include Web of linked data, Online lives, Web Evolving Technology, Protocols, Standards and Applications of Semantic Web with a focus on deploying and analyzing the web; Social Network Analysis, Cloud Computing, Web Services and Web Mining.

Course objective: In this course, students will learn the latest research development in the area of Web Science, semantic Web and its potential role in distributed data integration, retrieval management and processing. After taking this course, students are expected to know the state-of-the-art in Web Science and be able to continue research in this new area.

Mode of operation: Instructor and students will meet weekly in one 3-hr session. During each session, one student will deliver a lecture on a topic to be determined at the beginning of the semester. The presenter should inform the class the content and the relevant reading materials at least 3 days before the lecture. Everyone in the class are expected to study the materials before the class and participate in the class discussion.

Tentative Outline:
* Web Science: An Interdisciplinary Approach to understand the Web
* Web evolution
* Semantic Web development
* Social Network Analysis
* Web Service
* Web Mining
* Trend and Future evolution in Web Science
* and others

Textbooks: No textbook.

Grading: Students will be graded based on their presentation, and participation during class; and a survey report. Peer evaluation will be included.

References:
The recent papers related to this area, including the ones from World Wide Web Conference, WebSci (http://www.webscience.org), ACM WSDM, Web Semantics, ACM SIGMOD, Foundations and Trends in Web Science, etc.

Minimum CGA required for UG students: permission of the instructor

Course code: COMP696J
Course title: Independent Studies: Graphics Hardware
Instructor: Pedro V. Sander
Room: 3525
Telephone: 2358-6983
Email:
Quota: 4 (instructor's approval is needed for registration)
No. of credits: 3

Course description: In this course students will be expected to read, discuss, and contrast both academic research papers related to graphics hardware as well as white papers describing latest technology from graphics hardware manufacturers (Intel, AMD, Nvidia).

Course objective:
* Learn about the lastest graphics hardware features and products.
* Learn about the lastest graphics hardware academic research results.
* Analyze how the research results translate to hardware improvements and vice-versa.

Tentative Outline:
1. Research papers that drive new graphics hardware features
2. Latest graphics hardware architecture/features from Nvidia and AMD
3. New graphics hardware paradigm from Intel (and how it relates to above)
4. Research papers that use new graphics hardware features for a variety of applications

Textbooks: No textbook.

References:
None. Reading material consists of research papers and white papers from graphics manufacturers.

Grading Scheme:
Students will be accessed based on the weekly meetings and final report of their findings. There will be no exams.

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 Derek Hao Hu on 2010/02/18.