Fall 2009 CS Course Listings

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

Archive of past courses

Course code: COMP511
Course title: Fundamentals of Software Analysis
Instructor: S.C. Cheung and Charles Zhang
Room: 3537 and 3553
Telephone: 2358-7016 2358-6997
Email: ;
WWW page: http://course.cse.ust.hk/comp511

Area in which course can be counted: ST

Course description: see course catalog

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

Textbooks: 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: COMP522
Course title: Machine Learning
Instructor: Dit-Yan Yeung
Room: 3541
Telephone: 2358-6977
Email:
WWW page: http://cse.hkust.edu.hk/~dyyeung/

Area in which course can be counted: AI

Course description:
Machine learning is the science of making computer artifacts improve their performance without requiring humans to program their behavior explicitly. Machine learning has accomplished successes in a wide variety of challenging applications, ranging from computational molecular biology to computer vision to social web analysis. This course is a postgraduate-level introductory course in machine learning with emphasis put on the computational and mathematical principles underlying the most common machine learning problems and methods. It is not only suitable for students pursuing or planning to pursue research in machine learning or other related areas that focus on model and algorithm development, but is also suitable for students who want to apply disciplined machine learning techniques competently to their application-oriented research areas.

Course objective:
By the end of this course, students are expected to demonstrate ample competence in the following:
- Ability to take a real-world application and formulate the learning problems involved in it by identifying the major learning-related issues
- Ability to choose and apply the most common methods available for each of the major learning problem types
- Ability to compare different machine learning methods according to common performance criteria
- Ability to design and conduct empirical studies in such a way that the experimental results can be interpreted in accordance with disciplined scientific and statistical principles
- Ability to understand the motivations behind and the key issues studied in some recent research topics in machine learning

Course outline/content (by major topics):
** TENTATIVE TOPICS **

Overview of basic machine learning paradigms:
- Supervised learning
- Unsupervised learning
- Reinforcement learning

Bayesian decision theory

Supervised learning:
- Classification, regression, ordinal regression, ranking/preference, and dimensionality reduction problems
- Generative and discriminative methods
- Parametric and nonparametric methods

Unsupervised learning:
- Density estimation, clustering, and dimensionality reduction problems
- Parametric and nonparametric methods

Bayesian methods:
- Graphical models
- Hierarchical parametric and nonparametric methods
- Bayesian inference

Reinforcement learning:
- Value iteration
- Policy iteration
- Q-learning
- Temporal difference learning

General learning issues:
- Feature selection
- Model selection
- Model combination: bagging and boosting
- Computational learning theory

Survey of some recent research topics, e.g.
- Semi-supervised learning
- Active learning
- Transfer learning and multi-task learning
- Statistical relational learning
- Topic models

Textbooks:
1. Ethem Alpaydin (2004). Introduction to Machine Learning. MIT Press.
2. Christopher M. Bishop (2007). Pattern Recognition and Machine Learning. Springer.

Reference books/material:
1. Richard O. Duda, Peter E. Hart, and David G. Stork (2001). Pattern Classification. Second Edition. Wiley.
2. Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009). The Elements of Statistical Learning. Second Edition. Springer.
3. Tom M. Mitchell (1997). Machine Learning. McGraw-Hill.
4. Other assigned reading material

Grading scheme:
Class participation (10%)
Problem sets (20%)
Programming projects (20%)
Midterm exam (20%)
Final exam (30%)

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A-, or permission of instructor

Background needed:
Computer science: object-oriented programming and data structures, design and analysis of algorithms
Mathematics: calculus, linear algebra, probability and statistics

Course code: COMP524
Course title: Computer Vision
Instructor: C K Tang
Room: 3561
Telephone: 2358-8775
Email:
WWW page: http://course.cse.ust.hk/comp524 (CSD username/password to logon)

Area in which course can be counted: VG

Course description: see course catalog
Course objective: see http://course.cse.ust.hk/comp524
Course outline/content (by major topics): see http://course.cse.ust.hk/comp524

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

Reference books/materials:
Vision, David Marr, Freeman, 1982
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
Digital Picture Processing, A. Rosenfeld and A. Kak, Academic Press, 1982

Grading scheme:
Projects: 64%
Homeworks: 4%
Final Exam: 32%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A-

Course code: COMP530
Course title: Database Architecture and Implementation
Instructor: Wilfred Ng
Room: 3505
Telephone: 2358-6979
Email:
WWW page:

Area in which course can be counted: DB

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

Course objective:
Introductory database class for graduate students. The students are expected to learn basic
concepts and implementation techniques of relational databases and advanced RDBMS applications.

Course outline/content (by major topics):
The instructor will teach the majority of the classes. Students will form groups. Each group will choose a general database area and prepare a presentation.

Textbooks:
Textbook: Database System Concepts, 5th Edition. A. Silberschatz, H. Korth, and S. Sudarshan.

Reference books/materials:
Reference: Database Management Systems, 3rd Edition. Raghu Ramakrishnan and Johannes Gehrke.

Grading scheme:
Student Presentations 20%, Midterm 35%, Final 45%. 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.

Minimum CGA required for UG students: (can be "permission of the instructor")

Background needed: COMP252

Course code: COMP561
Course title: Computer Networks
Instructor: Yunhao Liu
Room: 3548
Telephone: 2358-7019
Email:
WWW Page: http://cse.hkust.edu.hk/~liu/comp561/index.htm

Area in which course can be counted: Networking & Computer Systems (NE)

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.

Textbooks:
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 COMP561, fall of 2009. 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: 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 objective:
To equip students with a broad knowledge of general techniques for designing and analyzing algorithms.

Course outline/content (by major topics):
o Network Flow
o Approximation Algorithms
o Randomized Algorithms

Textbooks:
Algorithm Design. Jon Kleinberg and Eva Tardos, Addison Wesley, 2005.

Reference books/materials:
Randomized Algorithms. Rajeev Motwani, Prabhakar Raghavan, Cambridge University Press, 1995.

Introduction to Algorithms (2nd Edition). T. Cormen, C. Leiserson, R.
Rivest, C. Stein. McGraw Hill and MIT Press, 2001.

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: COMP610I
Course title: Software Evolution and Repository Mining
Instructor: Sung Kim
Room: 3536
Telephone: 2358-6992
Email:
WWW page: http://cse.hkust.edu.hk/~hunkim/

Area in which course can be counted: ST

Course description (can be more detailed than the one in the calendar):
Software evolves and thus has history. Software development histories usually stored in SCM, issue-tracking systems, and mailing, have important information to improve the software development process, which is proven by many recent promising research results.

This class will give you good understanding of software evolution and techniques to utilize software histories. Based on the understanding, we will have hands-on experience on extracting histories and mining them to obtain useful information for future software development.

The class is based on the state-of-art research results. Discussions followed by each lecture will give you deep understanding on the topics covered by the lecture. A semester-long project will give you a unique opportunity to deal with real software history data and apply decent mining techniques to solve practical problems.

Course objective:
# Understand software evolution, repositories, and history
# Understand basic (graph) mining techniques
# Obtain ability to extract useful information from software history by mining

Course outline/content (by major topics):
* Extracting software history and repositories
* Software decay
* Graph representations of software
* Graph mining techniques
* Change assistance/suggestion
* Fault detection and prevention
* Machine learning techniques/Weka toolkit
* Developer or software social network analysis

Textbooks: TBA

Reference books/materials: TBA

Grading scheme:

* Participant 20%
* Paper review 30%
* Project: 50%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: None

Course code: COMP621O (Re-offering)
Course title: Kernel Methods in Machine Learning
Instructor: James Kwok
Room: 3519
Telephone: 2358-7013
Email:
WWW page: http://cse.hkust.edu.hk/~jamesk/

Area in which course can be counted: AI

Course description:
• Kernel methods (including the well-known support vector machines) is one of the most influential developments in modern machine learning.
• Various kernel-based techniques are now playing an increasingly important role in both machine learning and other areas, including data mining, bioinformatics, electronic commerce, speech and language understanding, computer vision, computer graphics, information retrieval, and decision support systems.
• This research-oriented course will introduce students to the basic concepts and some recent research topics in the field.
• Applications to real-world problems will serve as examples.

Course objective:
• The objective of this advanced topics course is to help research postgraduate students to keep abreast of some latest developments in modern machine learning, namely kernel methods.
• The course will be focused on familiarizing the student with a number of practical kernel-based algorithms (such as support vector machines, kernel principal components analysis) and a number of techniques to construct kernels (such as string kernels, graph kernels, marginalized kernels).
• Moreover, students will also learn novel applications to real-world problems that are made possible by the newly developed tools.
• Students who have successfully finished this course should be ready to apply kernel techniques to their respective research areas.

Course outline/content (by major topics):
• Major topics include kernel methods for supervised learning (e.g., support vector machines, support vector regression), unsupervised learning (e.g., kernel PCA, one-class support vector machines), semi-supervised learning and spectral methods. Kernel design.

Textbooks: No

Reference books/materials:
• Many recent research papers

Grading scheme: class participation + project

Background needed:
• Background in machine learning or pattern recognition (equivalent to COMP 522 and COMP 527) preferred though not essential

Exclusion (if applicable): n/a

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP621Q
Course title: Multiagent Systems
Instructor: Fangzhen Lin
Room: 3511
Telephone: 2358-6975
Email:
WWW page: http://cse.hkust.edu.hk/~flin/comp621q.html

Area in which course can be counted: AI

Course description (can be more detailed than the one in the calendar):
This course will cover game-theoretic and logic approaches to addressing issues arising in a multiagent system where multiple autonomous entities (agents), each with his own goal, interest and agenda, have to co-exist.

Course objective:
The objective is to prepare students to work in related areas. Concrete research problems will be discussed.

Course outline/content (by major topics):
- Introduction
- Distributed constraint satisfaction and optimization
- Noncooperative game theory
- Logical formalizations of game theory (introduction to propositional and first-order logics)
- Constraint-Based Problem Solving Systems
- Teams of selfish agents: coalitional games.
- Communication
- Aggregating preferences: social choice theory.
- Protocols for strategic agents: mechanism design
- Protocols for multiple resource allocation: auctions
- Forgetting, strongest necessary and weakest sufficient conditions, and belief merging
- Readings and student presentations (read recent proceedings of IJCAI, AAAI, KR, and others, and find a paper there for presentation)

Textbooks:
Multiagent Systems:  Algorithmic, Game-Theoretic, and Logical Foundations. Yoav Shoham and Kevin Leyton-Brown,  Cambridge University Press, 2009

Reference books/materials: To be provided.

Grading scheme: 50% for project, 15% for class attendance and involvement in discussions, and 35% for presentations.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP630K (Re-offering)
Course title: Advanced Topics in Database Research
Instructor: Dimitris Papadias
Room: 3555
Telephone: 2359-6971
Email:
WWW page: http://cse.hkust.edu.hk/~dimitris/

Area in which course can be counted: Databases

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

Course objective:
To introduce PG students to the state-of the art research on databases and related topics.

Course outline/content (by major topics):
1. Spatial and Spatio-temporal databases (for static objects)
1.1 Indexing techniques (R-trees based methods).
1.2 Spatial query processing in the Euclidean space.
1.3 Spatial query processing for road networks and obstacles.
1.4 Spatio-temporal aggregation techniques
1.6 Reverse nearest neighbors

2 Skylines and Top-k Queries
2.1 Query processing techniques for conventional databases
2.2 Probabilistic and uncertain skylines
2.3 Personalized skylines
2.4 Materialization methods
2.5 Stream processing of skylines and top-k queries
2.6 Distributed processing of preference queries

3 Privacy and Security in Databases
3.1 Anonymity and diversity
3.2 Location cloaking
3.3 Database outsourcing
3.4 Merkle-based structures
3.5 Order-preserving encodings
3.6 Alternative outsourcing models

4 Keyword search in Databases
4.1 Graph-based methods
4.2 Operator-based methods
4.3 Keyword search on relational streams
4.4 Ranking
4.5 Keyword search over distributed databases
4.6 Reachability indexing

5 Additional Topics
5.1 Query processing using new hardware
5.2 Clustering and related problems for large datasets
5.3 Data stream processing
5.4 Sensor networks
5.5 Private query processing
5.6 High dimensional spaces and the curse of dimensionality

Textbooks:

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%
Survey paper on selected topic: 25%
Participation and activity in class: 25%

Background needed:
Background in Databases helpful but not required.

Available for final year UG students to enroll: No

Minimum CGA required for UG students: NA

Course code: COMP630O
Course title: Managing Uncertain Databases
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):
Topics in managing uncertain databases, including uncertain data modeling, analyzing uncertain data, querying uncertain data, and indexing uncertain data.

Course objective:
1. Acquire broad knowledge in data management issues over uncertain data
2. Develop interest research topics on managing uncertain data

Course outline/content (by major topics):

Modeling Uncertain Databases
Analyzing Uncertain Data
Querying Uncertain Data
Indexing Uncertain Data
Uncertain Stream Data Processing
Distributed Uncertain Data Processing
Uncertain Graph/XML Data Processing
Quality Measures in Uncertain Data Management

Textbooks: None

Reference books/materials: Papers from the literature

Grading scheme:
Presentation: 40%
Course project: 60%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Background needed: COMP231

Course code: COMP641N
Course title: Topics in Graphics: Visual Analytics
Instructor: Huamin Qu
Room: 3508
Telephone: 2358-6985
Email:
WWW page: http://cse.hkust.edu.hk/~huamin/

Area in which course can be counted: Vision & Graphics

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

Visual Analytics is the science of analytical reasoning facilitated by highly interactive visual interfaces. Visual analytics techniques can help people derive insight from massive, dynamic, and often conflicting data; detect the expected and discover the unexpected; provide timely and understandable assessments; and communicate assessments effectively to a wide range of audiences for action. It is an interdisciplinary field and involves techniques from computer graphics, visualization, analytics, perception, cognition, statistics, data mining, and database. The course will cover recent research results in visual analytics and students will have opportunity to develop visual analytics tools to analyze their own data.

Course objective: To provide an overview of the state-of-the-art research on visual analytics; to explore new visual analytics techniques.

Course outline/content (by major topics):
The science of analytical reasoning Visual representations and interaction techniques
Data representations and transformations
Production, presentation, and dissemination
Geospatial data analysis
Social network analysis
Text visualization
Business data analysis

Textbooks: lecture notes and research papers

Reference books/materials: Illuminating the Path: The Research and Development Agenda for Visual Analytics.
Available online: http://nvac.pnl.gov/agenda.stm

Grading Scheme:
Paper presentations (30%)
Survey report (20%)
Final project (50%)

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: permission of the instructor

Course code: COMP660L
Course title: Topics in Computer & Communication Networks: Cloud Computing
Instructor: Lin Gu
Room: 3562
Telephone: 2358-6991
Email:
WWW page: http://cse.hkust.edu.hk/~lingu

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

Course description (can be more detailed than the one in the calendar):
After two waves of technological innovations and industrial development, the aggressive investment on information infrastructure has finally made Internet connections a mass product. Stable, high-bandwidth, and low-latency Internet connections are now available to a large number of users at affordable prices in many parts of the world. This trend has given rise to a new computing paradigm called "cloud computing". Integrating gigantic datacenters with geographically distributed user populations, cloud computing can provide high-quality services to individual and corporate users at very low cost. In the meantime, cloud computing also requires that we re-design many components of the computing systems, including hardware, operating systems, storage abstractions, data models, programming frameworks, development utilities, user interfaces, and software engineering practice, to deliver computation at such an unprecedentedly large scale.

Course objective:
This course will study organization of cloud computing systems and survey research problems in this area. This course will re-visit a number of components in computing systems, investigate their new design constraints in the cloud-computing environment, and study the state-of-the-art solutions by surveying both research papers and industrial developments. With a focus on system software design, many topics in cloud computing will be discussed, and a project based on a web service provides opportunities for hands-on experience on solving some real problems in this area.

Course outline/content (by major topics):
Topics include the system organization in a cloud computing environment, a survey on related works (grid computing, etc.), file systems, parallelization techniques, database semantics, network management, distributed services, and energy efficiency.

Textbooks: None.

Reference books/materials:
Instructor-prepared lecture materials and a 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, reading notes, 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

Background needed: knowledge on operating systems and computer networks.

Course code: COMP670R
Course title: Topics in Theory: Hashing
Instructor: Mordecai Golin and Ke Yi
Room: 3559 (Prof. Golin), 3552 (Prof. Yi)
Telephone: 2358-6993 (Prof. Golin), 2358-8770 (Prof. Yi)
Email: and
WWW page: http://cse.hkust.edu.hk/~golin and http://cse.hkust.edu.hk/~yike/

Area in which course can be counted: No area

Course description (can be more detailed than the one in the calendar):
A quick and dirty introduction to the basic concepts of Hashing followed by a survey of both old and recent work in computer science that uses hashing.

Course objectives:
To introduce students to an important area in modern computer science.

Course outline/content (by major topics):
The course will start with the instructors giving two or three introductory lessons on the basics of hashing. The remainder of the course will be students presenting papers. All students will be expected to read ALL of the papers and assigned to present at least two papers.

Textbooks: No textbook.

Reference books/materials: TBA

Grading scheme: TBA

Available for final year UG students to enroll: No

Pre-requisites / Background needed: COMP271 or equivalent and mathematical sophistication. Enrollment is only with permission of the instructors.

Course code: COMP696F
Course title: Independent Studies: Parallel and Distributed Databases
Abbreviated title: Parallel and Distributed DB
Instructor: Dr. Qiong Luo
Room: 3554
Telephone: 2358-6995
Email:
WWW page: http://cse.hkust.edu.hk/~luo/
Quota: 4 (instructor's approval is needed for registration)

Course description (can be more detailed than the one in the calendar):
This course reviews traditional parallel and distributed database architectures, and discusses the state of the art on data management issues in multicore processors and wide-area networks.

Course outline/content (by major topics):
Parallel and Distributed Database Architectures; Data Placement; Query Processing and Optimization; Distributed Transaction Management; Reliability and Replication; Databases on Multicore Processors; Distributed Databases on the Internet.

Reference books/materials:
Raghu Ramakrishnan and Johannes Gehrke. Database Management Systems. 3rd Edition.
Tamer Ozsu and Patrick Valduriez. Principles of Distributed Database System. 2nd Edition.
A collection of recent papers.

Grading scheme: 50% discussion, 50% project.

Available for final year UG students to enroll: Yes

Pre-requisites / Background needed: Enrollment is only with permission of the instructors.

Course code: COMP697I
Course title: Sequences for Communication Systems
Instructor: Cunsheng Ding
Room: 3518
Telephone: 2358-7021
Email:
WWW page: http://cse.hkust.edu.hk/faculty/cding
Quota: 2 (instructor's approval is needed for registration)

Course description (can be more detailed than the one in the calendar):
This is an independent study course for PG students who would do research into error correcting codes, CDMA communications systems, and cryptography. In this course, students will learn both the theory and applications of sequences in coding theory, CDMA communication systems and cryptography.

Course outline/content (by major topics):
Feedback shift registers, randomness measures on sequences, transforms of sequences, cyclic difference sets, sequences with low correlation, applications in CDMA systems, coding theory and cryptography.

Reference books/materials:
T.W. Cusick, C. Ding and A. Renvall, Stream Ciphers and Number Theory, Elsevier, 2004.
S.W. Golomb and G. Gong, Signal design for good correlation, Cambridge University Press, 2005.

Grading scheme: TBA

Available for final year UG students to enroll: No

Pre-requisites / Background needed: Algebra, finite fields, combinatorics, number theory. Enrollment is only with permission of the instructors.

Course code: COMP697K (instructor's approval is required for taking this course)
Course title: Independent Studies: Engineering of Cyber-Physical Systems
Instructor: Dr. Shing-Shi Cheung
Room: 3543
Telephone: 2358-7016
Email:
WWW page: http://cse.hkust.edu.hk/~scc/comp697k

Area in which course can be counted: No area

Course description (can be more detailed than the one in the calendar):
Students will be asked to conduct an independent study of the testing and analysis of cyber-physical systems. The course comprises literature reading and presentations.

Textbooks: No textbook.

Reference books/materials: TBA

Grading scheme: Presentations and reading reports

Course code: COMP697L (instructor's approval is required for taking this course)
Course title: Independent Studies: Bayesian Computation
Instructor: Prof. Dit-Yan Yeung
Room: 3541
Telephone: 2358-6977
Email:
WWW page: http://cse.hkust.edu.hk/~dyyeung
Quota: 1

Course description (can be more detailed than the one in the calendar):
This course is built on a prior background in Bayesian statistics and Bayesian methods for machine learning, such as that covered in COMP621P. It focuses on the computational aspects of Bayesian methods using the R system.

Textbooks: Jim Albert (2007). Bayesian Computation with R. Springer.

Reference books/materials: Andrew Gelman, John B. Carlin, Hal S. Stern and Donald B. Rubin (2004). Bayesian Data Analysis. 2nd Edition. Chapman & Hall.

Grading scheme: Reading assignments, presentations and discussions, programming projects.

Course code: COMP697M (instructor's approval is required for taking this course)
Course title: Independent Studies: Compressed Sensing for Networks
Abbreviated Title: CS for networks
Instructor: Prof. Qian Zhang
No. of credits: 3
Room: 3533
Telephone: 2358-8766
Email:
WWW page: http://cse.hkust.edu.hk/~qianzh
Quota: 8

Course description (can be more detailed than the one in the calendar):
This is an independent study course for PG students who would conduct research on the potential application about "compressed sensing" theory on wireless networks, including wireless sensor networks and cognitive radio networks. The course comprises literature reading and presentations.

Textbooks: No textbook.

Reference books/materials: The recent top conference papers related to this area, including the ones from ACM SIGCOMM, ACM MOBICOM, IEEE INFOCOM and IEEE ISIT.

Grading scheme: presentation and reading reports.

Available for final year UG students to enroll: No.

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 2009/09/06.