Spring 2008 CS Course Listings

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

Archive of past courses

Course Code: COMP 530
Course Title: Database Architecture and Implementation

Instructor: Dimitris Papadias
Room: 3503
Telephone: 23586971
Email:
WWW page: http://cse.hkust.edu.hk/~dimitris/

Area in which course can be counted: Database

Course description:
This course introduces concepts and implementation techniques in database management systems: disk and memory management; advanced access methods; implementation of relational operators; query processing and optimization; concurrency control and recovery. In addition, a few types of advanced DBMS applications are covered.

Course objective:
Systems-oriented introductory database class for graduate students. The students are expected to learn basic concepts and implementation techniques of relational databases.

Course outline/content (by major topics):

  • Introduction to the relational model and SQL
  • Logical database design
  • Disk and memory management
  • Access methods and indexing
  • Implementation of relational operators
  • Query processing and optimization
  • Concurrency control and recovery
  • Physical database design
  • Advanced Topics

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

Text book:
Database Management Systems, 3rd Edition. Raghu Ramakrishnan and Johannes Gehrke.

Reference:
Database System Concepts, 4th Edition. A. Silberschatz, H. Korth, and S. Sudarshan.

Grading Scheme:

  • Student Presentations 15%,
  • Survey Paper 15%,
  • Project 20%,
  • Midterm 20%,
  • Final 30%.

The exams will be with open books (any book or notes) and will be based on material explicitly covered during the classes.

Background needed: The students are expected to be comfortable with programming.

Available for final year UG students to enroll: No

Course Code: COMP561
Course Title: Computer Networks

Instructor: Dr Qian Zhang
Room: 3533
Telephone: 23588766
Email:
WWW page: http://cse.hkust.edu.hk/~qianzh/

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

Course description:
Principles, design and implementation of computer communication networks; network architecture and protocols, OSI reference model and TCP/IP networking architecture; Internet applications and requirements; transport protocols TCP and UDP; network layer protocols, IP, routing, multicasting and broadcasting; local area networks; data link and physical layer issues; TCP congestion control, quality of service, emerging trends in networking.

Course objective:
Systems-oriented introductory networking class for graduate students.

Course outline/content (by major topics):

  1. Computer Networks and the Internet
  2. Application Layer
  3. Transport Layer
  4. Network Layer and Routing
  5. Multicast and Application-level Multicast
  6. Mobile and Wireless Networks
  7. Multimedia Networking
  8. Internet Security

Text book: James F. Kurose and Keith W. Ross Computer Networks: A Top Down Approach, The Fourth Edition, Addison Wesley, 2007.

Reference books/materials: Alberto Leon-Garcia and Indra Widjaja, "Communication Networks: Fundamental Concepts and Key Architectures", The Second Edition, Mc Graw Hill, 2004.

A collection of papers from journals, conference proceedings.

Grading Scheme:

  • Homework 15 points
  • Presentation 15 points
  • Projects 35 points
  • Final Exam 35 points

Exclusion: COMP362

Available for final year UG students to enroll: Yes (with instructor’s approval)

Course Code: COMP610G
Course Title: Topics in Software Engineering: Computational Finance
Abbreviated Title: Computational Finance

Instructor: Dr Gary Chan
Room: 3507
Telephone: 23586990
Email:
WWW page: http://cse.hkust.edu.hk/~gchan/

Area in which course can be counted: Software and Applications

Course description:
Modelling and compuational techniques for financial asset pricing; vanilla and exotic options pricing; arbitrage; simluation methodologies and heories; asset price modelling; derivation of Black-Scholes equation; Monte-Carlo, variance reduction and numerical techniques for Black-Scholes solutions

Course objective:
To introduce modelling and computational techniques for financial asset pricing to CSE students.

Course outline/content (by major topics):

  • Options basics
  • Arbitrage and options price bounds
  • Simulation techniques
  • Asset price models
  • Derivation of Black-Scholes equation
  • Solutions of Black-Scholes PDE with Monte Carlo and numerical techniques

Text book:
No fixed text-books

Reference books/materials: Various ones, introduced in class

Grading Scheme:

  • Presentation (50%)
  • Homework assignments (50%)

Available for final year UG students to enroll: No.

Minimum CGA required for UG students: No

Course Code: COMP 621P
Course Title: Advanced Topics in AI: Beyond Introductory Machine Learning
Abbreviated Title: Beyond Introductory ML

Instructor: Dit-Yan Yeung
Room: 3541
Telephone: 23586977
Email:
WWW page: http://cse.hkust.edu.hk/~dyyeung/

Area in which course can be counted: AI

Course description:
Machine learning is playing an increasingly important role in both artificial intelligence and other areas, including speech and language understanding, computer vision, computer graphics, information retrieval, data mining, Internet computing, computer forensics, bioinformatics, financial engineering, electronic commerce, social networks, and many others. This course may be seen as the continuation of an introductory machine learning course, such as COMP522, to study in greater depth some important topics of current interest to the machine learning community and other related application areas.

Course objective:
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. This course is not only useful to students pursuing research in machine learning, but is also useful to those working in other areas who want to apply advanced machine learning methods to their research.

Course outline/content (by major topics):

  • Kernel methods
  • Ensemble methods
  • Graphical models
  • Bayesian methods
  • Semi-supervised learning

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

Reference books/materials:
Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2001). The Elements of Statistical Learning. Springer.
Bernhard Schlkopf and Alexander J. Smola (2002). Learning with Kernels. MIT Press.

Grading Scheme:

  • Attendance (~10%)
  • Assignments (~30%)
  • Project/paper (~50%)
  • Presentation (~10%)

Prerequisite: COMP522 (or consent of instructor)

Available for final year UG students to enroll: No

Course Code: COMP630M
Course Title: Data Management on New Hardware
Abbreviated Title: Data Mgmt on New Hardware

Instructor:Qiong Luo
Room: 3554
Telephone: 23586995
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:

  • (T.B.A.)

Available for final year UG students to enroll: Yes.

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

Course Code: COMP641M
Course Title: Topics in Graphics: GPU Computation

Instructor: Pedro V. Sander
Room: 3535
Telephone: 23586983
Email:
WWW page: http://cse.hkust.edu.hk/~psander/

Area in which course can be counted: Vision & Graphics

Course description:
The Graphics Processing Unit (GPU) is particularly efficient for real-time rendering applications. Recently, it has also evolved into a highly parallel general purpose processor. In this course, we will cover recent research on GPU computation. In the first part, we present an in-depth analysis of the graphics hardware pipeline, including the most recent advances, such as geometry shaders. In the second part, we study recent, complex real-time rendering algorithms that take advantage of the added efficiency and functionality in order to render compelling 3D scenes in real time. Topics will include latest algorithms on lighting, shadowing, and shading. Finally, in the third part, we will study techniques that use the graphics pipeline for general purpose (non-rendering) computation. The research focus is on designing parallel algorithms that map efficiently to the GPU's graphics pipeline and are faster than their CPU counterparts. Applications include scientific algorithms, database query processing, and geometric optimization, among many others.

Course objective:
Students taking this course will gain an in-depth understanding of the GPU and learn to research and develop novel rendering algorithms and general purpose processing algorithms that run on the GPU.

Course outline/content (by major topics):

  • Overview of the Graphics Processing Unit (GPU)
  • Rendering algorithms
  • General purpose processing algorithms (GPGPU)

Text book: None

Reference books/materials: Research papers and course notes distributed by instructor

Grading Scheme:

  • Lab assignments (35%)
  • Class presentation (15%)
  • Final project (50%)

Background needed: Basic computer graphics background equivalent to COMP 341 is highly recommended, but not required for very strong PG students that are interested in GPGPU.

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

Instructor: Prof. Dik Lun Lee
Room: 3534
Telephone: 23587017
Email:
WWW page:http://cse.hkust.edu.hk/~dlee/


Area in which course can be counted:

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

Text book: N/A.

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

Grading Scheme: (T.B.A.)

Background needed: Permission of the instructor

Available for final year UG students to enroll: No

Course Code: COMP670Q
Course Title: Topics in TH: I/O-Efficient Algorithms and Data Structures
Abbreviated Title: Topics in TH:I/O-Eff Algo

Instructor: Ke Yi
Room: 3552
Telephone: 23588770
Email:
WWW page:http://cse.hkust.edu.hk/~yike/


Area in which course can be counted: TH

Course description (can be more detailed than the one in the calendar):
In many modern applications, such as large-scale databases, massive data sets often need to be stored and managed on external storage devices. In order to manipulate the data and perform the computation efficiently in external memory, the algorithms and data structures need to adopt I/O-efficient techniques. The course will provide an overview of the development of I/O-efficient algorithms in the past two decades, and will also dive into selected topics.

Course objective:
Students will be introduced to many I/O-efficient techniques and how they can be applied to solve various problems. The course will focus on design and theoretical analysis of the algorithms, but will also touch on their implementation and practical performance.

Course outline/content (by major topics):

  • Hierarchical memory models and fundamental bounds
  • External sorting and searching, B-trees
  • Geometric searching problems
  • Batched geometric problems
  • Graph problems
  • Cache-oblivious algorithms
  • Implementation of I/O-efficient algorithms and data structures

Text book: N/A.

Reference books/materials: Papers and handouts

Grading Scheme (Tentative):

  • 50% assignment
  • 50% project

Background needed: COMP271 or equivalent

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: permission of the instructor

Course Code: COMP 680M
Course Title: Topics in Computer Engineering: Multicore Computing

Instructor: Lionel M. Ni
Room: 3501
Telephone: 2358-7009
Email:
WWW page:http://cse.hkust.edu.hk/~ni/

Area in which course can be counted: Computer Engineering

Course description:
A multicore microprocessor (or chip-level multiprocessor, CMP) combines two or more independent cores into a single physical package. While both dual-core and quad-core processors are available in market, processors with hundreds of cores are expected in the future. This course will discuss new challenging issues in multicore computing, including multicore architecture, software design, programming, and tools.

Course objective:
This course will study the architecture, software design, programming, and tools in multicore computing. The focus will be on new challenging research issues so postgraduate students will have a better understanding about this new technology and the hurdles to fully explore the large computing power.

Course outline/content (by major topics):

Topics include multicore architecture, multithread programming, OS design, software design, tools, and some applications.

Text book: N/A.

Reference books/materials: collection of research papers

Grading Scheme (Tentative):

We will meet once 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: No

Course Code: COMP 696S
Course Title: Independent Studies: Information Visualization

Instructor: Dr Huamin Qu
Room: 3508
Telephone: 2358-6985
Email:
WWW page: http://www.huamin.org/

Area in which course can be counted:

Course description:
Information visualization is emerging as a hot research field which fuses computer graphics, cognitive psychology, data mining, graphic design, and human-computer interaction. This course aims to provide an in-depth view of some advanced topics in information visualization. Topics include human perception, graph drawing algorithms, multivariate data encodings, network visualization, and user studies. The coursework will include readings, seminar presentations, and a real information visualization project.

Course objective:
To provide an introduction to information visualization; to gain an in-depth view of some advanced topics in information visualization.

Course outline/content (by major topics):

Human Perception
Treemaps
Graph Drawing Algorithms
Network Visualization
Multivariate Data Visualization
User Studies

Text book:

Collin Ware, "Information Visualization: Perception for Design", 2nd Edition, Morgan Kaufman Publishers/ 2004
Chaomei Chen, "Information Visualization: Beyond the Horizon", 2nd Edition, Springer/2004

Reference books/materials: Research papers

Grading Scheme (Tentative):

30% Discussion
20% Presentations
50% Project

Background needed: COMP341 (Computer Graphics)

Available for final year UG students to enroll: No

Course Code: COMP 697N
Course Title: Independent Studies: Information Visualization

Instructor: Dr Brahim Bensaou
Room: 3537
Telephone: 2358-7014
Email:
WWW page: http://cse.hkust.edu.hk/~csbb/

Quota: 2 (instructor’s approval is needed for taking the course)

Area in which course can be counted:

Course description:
The course will cover practical aspects of network protocols design and implementation. In their UG studies, students study mainly how to develop Application level protocols using Berkley sockets. Nowadays, the network architecture protocol architecture is getting more complex, and concepts such as cross-layer protocol design are becoming more pervasive. This course provides the students with a systematic approach to developing lower layer network protocols and teaches them advanced programming and debugging technique involved in the development of network device drivers. The course will be illustrated by real examples from the Linux architecture.

Course outline/content (by major topics):

  1. The Kernel Structure
  2. Introduction to Device Drivers & Kernel Modules
  3. Managing Network Packets in the Kernel
  4. Kernel Debugging Techniques
  5. The Internet Protocol V4 and IP Routing
  6. Allocating and managing Memory in the Kernel
  7. Communicating with Hardware
  8. Transmission Control Protocol (TCP)
  9. The Linux Device Model
  10. Memory Mapping & DMA
  11. Network device Drivers

Text book:

Klaus Wehrle, Frank Pählke, Hartmut Ritter, Daniel Müller and Marc Bechler, The Linux® Networking Architecture: Design and Implementation of Network Protocols in the Linux Kernel, 2004

And various open source references

Grading Scheme:

There will be no exam, the students will meet with the instructor on a weekly basis and are assessed on a continuous basis through direct interaction.

Background needed: COMP252, COMP361

Available for final year UG students to enroll: No

Course Code: COMP 697P
Course Title: Independent Studies: Automatic Speech Recognition
Abbreviated title: Speech Recognition

Instructor: Dr. Brian Mak
Room: 3513
Telephone: 2358-7012
Email:
WWW page: http://cse.hkust.edu.hk/~mak/

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

Area in which course can be counted:

Course description:
The course is designed for students with some elementary knowledge of pattern recognition, such as Bayesian classification, EM algorithm, and hidden Markov modeling. Students will apply their knowledge of pattern classification on Automatic Speech Recognition, with an emphasis on acoustic modeling, speaker adaptation, and discriminative training.

Course outline/content (by major topics):

  1. Review of linear algebra, probabilities and statistics, and pattern classification
  2. Speech production and perception
  3. Density estimation
  4. Review of HMM
  5. Discriminative training
  6. Adaptation

Text book: Nil

Reference books/materials: Xue-Dong Huang, Alex Acero, and Hsiao-Wuen Hon "*Spoken Language Processing*" Prentice Hall PTR, 2001.

Grading Scheme:

project and presentation

Background needed: Permission of the instructor

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 Joshua M. Y. Chan on 5 Feb, 2008.