Spring 2011 CS Course Listings

This file contains the Spring 2011 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP511
Course title: Fundamentals of Software Analysis
Instructor:Charles Zhang
Room: 3553
Telephone: 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: COMP521
Course title: Advanced Artificial Intelligence
Instructor: Dr Fangzhen Lin
Room: 3511
Telephone: 23586975
Email:
WWW page: http://cse.hkust.edu.hk/~flin/

Area in which course can be counted: AI

Course description:
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

Textbooks:
Stuart Russell and Peter Norvig. Artificial Intelligence ¨C A Modern Approach. Prentice Hall, 2003.

Reference books/materials:TBA

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP524
Course title: Computer Vision
Instructor: C K Tang
Room: 3561
Telephone: 23588775
Email:
WWW page: http://cse.hkust.edu.hk/~cktang/

Area in which course can be counted: VG

Course description: Same as listed in the course catalogue/academic calendar

Course objective:
COMP524 is a 500-level graduate course in computer vision assuming no UG/PG computer vision background. It can be used to satisfy the VG core requirement.

Course outline/content (by major topics):
1 Introduction
2 Image formation
3 Image filtering
4 Edge detection
5 Segmentation
6 Segmentation II
7 Texture
8 Projective geometry (handout)
9 Image warping
10 Stereo
11 Disparity by graph-cut
12 Surface from Stereo (Tensor voting)
13 Multiview stereo
14 Light
15 Photometric stereo
16 Optical flow
17 Structure from Motion

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

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

Grading scheme:
The breakdown is subject to change as a whole and adjustments on a per-student basis in exceptional cases.
This is the general breakdown we'll be using for Scheme 1:

Projects: 64%
Homeworks: 4%
Final Exam (Oral): 32%

Grading Scheme 2 targets at students in other research areas who need to fulfil the Vision/Graphics core requirement.
The tentative breakdown for students signing up for Scheme 2 is as follow:

Project #1 and Papers Critique: 26%
Homeworks: 4%
Final Exam (Written): 70%

The two schemes will be described during the first and/or second lecture in September.

Computer projects and papers critique will be done in teams up to two students (three-student team is not permitted).

Homeworks are to be completed individually. Though you may discuss the problems with others, your answers must be your own.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A- and permission of the instructor

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: NT

Course description:
This course discusses the advanced principles in computer and communication networking. More particularly, the following topics will be addressed during this course, including 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) Review of the basic principles of computer networking
2) Broadcasting and Multicasting
3) Peer-to-Peer Networking
4) Wireless Networking
5) Multimedia Networking and Quality of Service Provision
6) Advanced Topics for Congestion Control
7) Network Security
8) Student Presentation

Text book: A collection of papers from journals, conference proceedings, and website need to be read.

Reference books/materials:TBA

Grading scheme:
Homework 30 points
Paper Presentation 15 points
Project Report 20 points
Final Exam 35 points

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: A-

Pre-requisites: COMP361/ELEC315/COMP561

Course code: COMP572
Course title: Introduction to Combinatorial Optimization
Instructor: Mordecai Golin
Room: 3559
Telephone: 23586993
Email:
WWW page: http://cse.hkust.edu.hk/~golin

Area in which course can be counted: TH

Course description:
* An introduction to the basic tools of Combinatorial Optimization.
* Includes: Network flow and the Max-Flow Min cut Theorem, Linear Programming, Matching, Spanning Trees and Matroids, Dynamic Programming and Basic Graph Algorithms.

Course objective:
* Upon completion of this course students will have been introduced to many of the most basic tools of combinatorial optimization and will be able to apply them towards designing efficient algorithms in their own research domains.

Text book:
* Combinatorial Optimization : Algorithms and Complexity Christos H. Papadimitriou and Kenneth Steiglitz, Dover books, 1998

Grading scheme: TBA

Background needed:
* COMP271 or equivalent + Linear Algebra

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: Prof. Cunsheng Ding
Room: 3518
Telephone: 23587021
Email:
WWW page: http://cse.hkust.edu.hk/faculty/cding/COMP581/

Area in which course can be counted: ST

Course description:
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

Textbooks:
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, instructor's approval only

Background needed: Basic knowledge of computer networks

Course code: COMP621R
Course title: Automatic Speech Recognition in Practice
Abbreviated Title: IASR in Practice
Instructor: Brian Mak
Room: 3513
Telephone: 2358-7012
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. Adaptation and adaptive training
8. Discriminative training
9. Finite state transduction
10. 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: Generally 10 with permission of the instructor

Course code: COMP621U
Course title: Social Networks, Media and Transfer Learning
Abbreviated Title: Soc NW,Media&Trans Learn
Instructor: Qiang Yang
Room: 3563
Telephone: 23588768
Email:
WWW page: http://cse.hkust.edu.hk/~qyang/

Area in which course can be counted: AI

Course description:
This course surveys recent development in data mining and machine learning in the areas of social networks and social media, and transfer learning.
We will first give an introduction to each of these fields. We then survey the recent developments in these fields. Finally, we will engage in some research projects in these areas on some real data.

Course objective:
1. To introduce the concepts of data mining in social network analysis, social media and finally, transfer learning.
2. To help students gain knowledge in research in these areas.

Course outline/content (by major topics):
1. Overview of Social Network Data Mining
2. Overview of Social Media Data Mining
3. Overview of Transfer Learning
4. Selected readings in Social networks, social media and transfer learning

Reference books/materials: Online resources, conference and journal proceedings.

Grading scheme:
10%: participation
40%: presentation
50%: proposal and completion of course project

Available for final year UG students to enroll: No.

Minimum CGA required for UG students: permission of the instructor

Course code: COMP630Q
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:
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: COMP641P
Course title: Topics in Graphics: Real-Time Rendering
Instructor: Pedro V. Sander
Room: 3535
Telephone: 2358-6983
Email:
WWW page: http://cse.hkust.edu.hk/~psander

Area in which course can be counted: Vision & Graphics

Course description:
In this course we present an in-depth analysis of the graphics hardware pipeline, including the most recent advances, such as geometry shaders and hardware tesselation. We then 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 geometry processing, lighting, shadowing, and shading on the GPU.

Course objective:
Students taking this course will gain an in-depth understanding of real-time rendering algorithms and how to program the GPU.

Course outline/content (by major topics):
-Overview of the Graphics Processing Unit (GPU) and rendering pipeline
-Recent research on geometry processing
-Recent research on lighting
-Recent research on shadowing
-Recent research on shading

Textbooks: None

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

Grading scheme:
-Lab Assignments (15%)
-Class Presentation (25%)
-Final project (60%)
(exact percentages subject to change)


Pre-requisites/Background needed:
Basic computer graphics background equivalent to COMP 341 is highly recommended, but not required for very strong PG students.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

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 Tong Zhu on 2010/11/12.