Fall 2015 CS Course Listings

This file contains the Fall 2015 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:
* Conferences: Proceedings of ICSE, FSE, PLDI, OOPSLA, ISSTA and ASE.
* Journals: ACM TOSEM & IEEE TSE.
* Software Engineering, Ivan Marsic, Rutgers University, 2012. (Download here)
* Introduction to Software Testing, Paul Ammann and Jeff Offutt, Cambridge University Press, 2008.
* Software Testing and Analysis: Process, Principles and Techniques, Mauro Pezze and Michal Young, John Wiley and Sons, 2007.
* Head First Java, Kathy Sierra and Bert Bates, O'Reilly Media, Inc.
* Head First Design Patterns, Eric Freeman, Elisabeth Robson, Bert Bates, Kathy Sierra, O'Reilly Media, Inc.
* Design Patterns, Enrich Gamma, et al, Addison-Wesley, 1995.

Grading scheme:

Class Participation 5%
Assignments 40%
Reading Report, Presentation & Participation 15%
Final Exam 40%


Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP5211
Course title: Advanced Artificial Intelligence
Instructor: Fangzhen Lin
Room: 3511
Telephone: 2358-6775
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: A Modern Approach Prentice Hall, 2003.

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: 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: COMP5621
Course title: Computer Networks
Instructor: Qian Zhang
Room: 3533
Telephone: 2358-8766
Email:
WWW Page:

Area in which course can be counted: NT

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; wireless and mobile networking, multimedia networking.

Exclusion: COMP4622

Course objective:
Upon completion of this course you will have an in depth knowledge about the foundations of current Internet applications, serviced and architecture and will learn about some of the challenges that are defining the future trends in the design of new services and protocols for the Internet.

Course outline/content (by major topics):

Textbooks:
*James Kurose and Keith Ross, Computer Networking: A Top Down Approach, (6th Ed.), Pearson, 2009.
*A collection of papers and articles provided as a reading list.

Reference books/materials:

Grading scheme:
Homework, paper presentation, and Final Exam.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Instructor Permission required

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:
2 Midterm exam: 25% * 2
Final exam: 50%

Available for final year UG students to enroll: Yes

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

Course code: COMP6613A
Course title: Topics in Applications of Computer Science and Engineering: Hot Topics in Human-Computer Interaction
Instructor: Xiaojuan Ma
Room: 3547
Telephone: 2358 6991
Email:
WWW Page:
Area in which course can be counted: Software and Applications

Course description:
This course is a broad post-graduate-level introduction to Human-Computer Interaction (HCI), with an emphasis on techniques, models, and theories for designing, prototyping, and evaluating current and future interactive systems for human use. Selected topics include (novel/natural) interaction design, usability evaluation, social computing, ubiquitous/mobile computing, virtual/augmented reality and gaming, agents and robots, etc.

Course objective:
Upon the completion of the course, students will 1) gain an understanding of the history and scope of HCI research; 2) get familiar with the foundations and trends in HCI; and 3) learn basic methods for designing, prototyping, and evaluating interactive systems.

Course outline/content (by major topics):
* Introduction & History of HCI: the Human, the Computer, and the Interaction
* Usability Evaluation and beyond
* Interaction Techniques: with Machines and Data
* Ubiquitous and Mobile Computing
* Computer Supported Cooperative Work and Computer Mediated Communication
* Accessible Computing and Assistive Technologies
* Interaction Design and Prototyping Techniques
* Social Computing
* Crowd Computing
* Affective Computing and Persuasive Technologies
* Virtual/Augmented Reality and Gaming
* Agents and Robots
* From Lab to Market

Textbooks:
Alan Dix, Janet Finlay, Gregory Abowd & Russell Beale. Human-Computer Interaction (3rd Edition). Prentice Hall, 2004. ISBN 0-13-046109-1.***
***Note: the textbook only covers a fraction of the topics. Additional readings will be assigned.

Reference books/materials:
* Yvonne Rogers, Heken Sharp, & Jenny Preece. Interaction Design: Beyond Human-Computer Interaction (3rd Edition). John Wiley & Sons, Inc, 2011. ISBN 0-470-66576-9, 978-0-470-66576-3.
* Ben Shneiderman and Catherine Plaisant. Designing the User Interface: Strategies for Effective Human-Computer Interaction (5th Edition). Reading, MA: Addison-Wesley Publishing Co. 2009. ISBN 0-321-53735-1.
* Donald A. Norman. The Design of Everyday Things. Basic Books, 2002.

Grading scheme:
* Weekly reading notes: 40%
* In-class presentation: 35%
* Final proposal: 20%
* Other: 5%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Please visit here for the timetable and quota.

Archive of past courses

Last modified by Qing Chen and Bo Liu on 2015-8-19.