Fall 2014 CS Course Listings

This file contains the Fall 2014 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP5111
Course title: Fundamentals of Software Analysis
Instructor: Shing-Chi Cheung
Room: 3543
Telephone: 2358-7016
Email:
WWW Page:

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:

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: COMP5213
Course title: Introduction to Bayesian Networks
Instructor: Nevin L. Zhang
Room: 4472
Telephone: 2358-7015
Email:
WWW Page: http://cse.hkust.edu.hk/~lzhang/teach/5213/

Area in which course can be counted: AI

Course description:
This course covers probabilistic models for unsupervised learning, including Bayesian networks for general probabilistic modeling, mixture models for cluster analysis, latent tree models for multidimensional clustering, factor models for dimension reduction, topic models for text modeling, and latent trait models for student modeling. The first topic will be discussed at depth, while the others at high level.

Course objective:
Provide students with a solid foundation in some probabilistic models for unsupervised learning.

Course outline/content (by major topics):
* Preparation:
o Introduction to course
o Multivariate Probability and Information Theory
* Bayesian networks:
o Basic concepts
o Inference
o Parameter learning
o Structure learning
* Mixture models
* Latent tree models
* Factor models
* Topic models
* Latent trait models

Textbooks:
* D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. edited by MIT Press, 2009.
* C. Bishop. Pattern Recognition and Machine Learning. Springer. 2006.
* N.L. Zhang and H.P. Guo. Introduction to Bayesian Networks. Science Press, Beijing, 2007.

Reference books/materials:

Grading scheme:
* Class participation: 10 (Based on impression. So make yourself heard in class.)
* Final exam: 50 (Required for all PG Corse courses)
* Project report: 30 (20 content & depth, 5 organization and presentation, 5 English )
* Oral presentation: 10

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A-

Course code: COMP5331
Course title: Knowledge Discovery in Databases
Instructor: Lei Chen
Room: 3542
Telephone: 2358-6980
Email:
WWW Page: http://cse.hkust.edu.hk/~leichen/

Area in which course can be counted: DB or AI

Course description:
Data mining has emerged as a major frontier field of study in recent years. Aimed at extracting useful and interesting patterns and knowledge from large data repositories such as databases and the Web, the field of data mining integrates techniques from database, statistics and artificial intelligence. This course will provide a broad overview of the field, preparing the students with the ability to conduct research in the field.

Background: COMP271

Course objective:
To learn the techniques used in data mining research. To help the students get ready for research.

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

Textbooks:
Data Mining: Concepts and Techniques. Jiawei Han, Micheline Kamber and Jian Pei. Morgan Kaufmann Publishers (3rd edition).

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

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

Available for final year UG students to enroll: Yes but with approval

Minimum CGA required for UG students: None

Course code: COMP5411
Course title: Advanced Computer Graphics
Instructor: Chiew-Lan Tai and Pedro Sander
Room: 3515; 3525
Telephone: 2358-7020; 2358-6983
Email: ,
WWW Page: http://course.cse.ust.hk/comp5411

Area in which course can be counted: VG

Course description:

Course objective:
Computer Graphics studies the principles of generating and displaying 3D images on the computer display. This course will first cover advanced topics in modeling and processing geometric shapes, and then topics on geometry rendering, lighting, and shading, using latest generation graphics hardware.

Exclusion: CSIT5400

Background: COMP3711, Linear Algebra, Calculus

Course outline/content (by major topics):
Basics of Computer Graphics
Curves and surfaces (Bezier, b-spline, implicit surfaces)
Discrete differential geometry
Differential methods for shape editing
Space-based deformation
Surface simplification
Surface smoothing
Graphics Processing Unit (GPU)
Programmable Rendering Pipeline (Vertex, Geometry, and Pixel shaders)
Surface lighting and shading
Real-time shadow algorithms
Global illumination
Future trends on GPU computing

Textbooks:
Dave Shreiner. OpenGL Programming Guide. Seventh Edition. Adisson Wesley. (optional reference book)

Reference books/materials:

Grading scheme:
Based on class participation, assignments and exams.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP5622
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: NE

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; wireless and mobile computing; advanced topics for wireless networking; multimedia networking and quality of service; introduction to network security and wireless security; wireless sensor and senor networks.

Background: COMP361/ELEC315/COMP561

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) Basis about Wireless and Mobile Computing
4) Advanced topics for wireless networking (multi-channel, wireless TCP, multi-hop networks)
5) Multimedia Networking and Quality of Service Provision
6) Network Security and Wireless Security
7) Wireless sensor and sensor networks
8) Student Presentation

Textbooks:
Computer Networking: A Top-Down Approach (6th Edition), James F. Kurose and Keith W. Ross.
A collection of papers from journals, conference proceedings, and website need to be read.

Reference books/materials:
TBA

Grading scheme:
Homework 20 points
Paper Presentation 20 points
Project Report 20 points
Final Exam 40 points

Available for final year UG students to enroll: Yes.

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

Course code: COMP5711
Course title: Introduction to Advanced Algorithmic Techniques
Instructor: Ke Yi
Room: 3552
Telephone: 2358-8770
Email:
WWW Page: http://cse.hkust.edu.hk/~yike

Area in which course can be counted: TH

Course description:
This is an introductory graduate course in algorithmic techniques.

Background: COMP3711, Discrete Mathematics, Probability

Course objective:
To equip students with a broad knowledge of general techniques for designing and analyzing algorithms.

Course outline/content (by major topics):
Fixed-parameter algorithms
Approximation algorithms
Local search
Amortized analysis
Randomized algorithms
Streaming algorithms
External-memory algorithms
Parallel and distributed algorithms

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

Reference books/materials:
- Introduction to Algorithms (3rd Edition). T. Cormen, C. Leiserson, R. Rivest, C. Stein. McGraw Hill and MIT Press.
- Randomized Algorithms. Rajeev Motwani, Prabhakar Raghavan, Cambridge University Press, 1995.

Grading scheme:
Homework: 20%
Midterm exam: 30%
Final exam: 50%

Available for final year UG students to enroll: Yes

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

Please visit here for the timetable and quota.

Archive of past courses

Last modified by Zhiyang Su on 2014-08-25.