Spring 2017 CS Course Listings

This file contains the Spring 2017 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP5112
Course title: Parallel Programming
Instructor: Qiong Luo
Room: 3554
Telephone: 2358-6995
WWW Page:

Area in which course can be counted: ST

Course description:
Introduction to parallel computer architectures; principles of parallel algorithm design; shared-memory programming models; message passing programming models used for cluster computing; data-parallel programming models for GPUs; case studies of parallel algorithms, systems, and applications; hands-on experience with writing parallel programs for tasks of interest. 
Exclusion(s): COMP 6111B, COMP 6511A, COMP 6611A
Background: COMP 3511 AND COMP 3711/COMP 3711H 

Course objective:
Students will attain the following on completion of the course:
* knowledge of parallel computer architectures;
* understanding of principles of parallel algorithm design;
* knowledge of shared-memory and distributed-memory programming models;
* hands-on experience writing parallel programs of a task of interest. 

Course outline/content (by major topics):
Introduction to parallel computer architectures;
principles of parallel algorithm design;
shared-memory programming models;
message passing programming models used for cluster computing;
data-parallel programming models for GPUs;
case studies of parallel algorithms, systems, and applications. 


Reference books/materials:
* Introduction to Parallel Computing, Second Edition. Ananth Grama, George Karypis, Vipin Kumar, Anshul Gupta. Addison-Wesley, 2003, ISBN: 0201648652. 
* Programming Massively Parallel Processors: A Hands-on Approach. Second Edition. David B. Kirk and Wen-mei W. Hwu. Elsevier 2012. ISBN: 978-0124159921. 

Grading scheme:
project 50%, final exam 50%

Course code: COMP5213
Course title: Introduction to Bayesian Networks
Instructor: Nevin L. Zhang
Room: 2404
Telephone: 2358-7015
WWW Page: https://course.cse.ust.hk/comp5213/

Area in which course can be counted: AI

Course description:
Probabilistic models commonly used in unsupervised machine learning, including Bayesian networks, mixture models and latent tree models, topic models, Boltzmann machines and deep belief networks. Criteria for model selection such as the Bayes information criterion. Methods for parameter estimation such as the EM algorithm, gradient descent, Bayesian estimation, variational inference, sampling, method of moments.

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 
* Topic models 
* Boltzmann machines and deep belief networks

A set of detailed lecture notes will be provided.
Reference books/materials: * K. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press, 2012.
* N.L. Zhang and H.P. Guo. Introduction to Bayesian Networks. Science Press, Beijing, 2007. 

Grading scheme:
* Homework: 10%
* Final Exam: 90% (Required for all PG Core courses.)
(Subject to change if the enrollment is below 20. In that case, a term project will be introduced.)
Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A-

Course code: COMP5311
Course title: Database Architecture and Implementation
Instructor:  Dimitris Papadias(send e-mail for questions regarding the class and for arranging individual meetings)
Room: 3555
Telephone: 2358-6971
WWW Page:

Area in which course can be counted: DB

Course description:
Introduction to the relational model and SQL. System architectures and implementation techniques of database management systems: disk and memory management, access methods, implementation of relational operators, query processing and optimization, transaction management and recovery.
Exclusion: COMP 3511

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. 
The course will be divided in two parts: 
(i) Background material to be taught by the instructor
(ii) New specialized topics to be presented by students.
Students will form groups and in addition to their presentation, they will have to submit a survey on the topic by the end of the semester. 

Background material (by major topics):
* E/R Model 
* Relational Model and Algebra
* Functional Dependencies and Relational Database Design
* File Systems
* Tree and Hash Indexes
* Query Processing and Implementation of Relational Operators
* Query Optimization
* Transactions 
* Database System Concepts, A. Silberschatz, H. Korth, and S. Sudarshan.
Reference books/materials: 
* Database Management Systems, Raghu Ramakrishnan and Johannes Gehrke.

Grading scheme:
50% final exam, 20% presentation, 20% survey, 10% class participation 
Available for final year UG students to enroll: No.

Course code: COMP5421
Course title: Computer Vision
Instructor: Quan Long
Room: 3506
Telephone: 7018
WWW Page: http://www.cse.ust.hk/~quan/

Area in which course can be counted: VG

Course description:
Introduction to techniques for automatically describing visual data and tools for image analysis; perception of spatial organization; models of general purpose vision systems; computational and psychological models of perception. 
Background: COMP3211 knowledge in linear algebra. 

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

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

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

Reference books/materials:
* Three-Dimensional Computer Vision, O. Faugeras, MIT Press, 1993
* 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: COMP5622
Course title: Advanced Computer Communications and Networking
Instructor: Qian Zhang
Room: 3533
Telephone: 2358 8766
Email: WWW Page: http://www.cse.ust.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; P2P networking; advanced topics for wireless networking; wireless sensor and senor networks; introduction to network security and wireless security; advanced topics related to congestion control. 

Pre-requisites: 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) P2P Networking
4) Advanced topics for wireless networking (multi-channel, multi-hop networks)
5) Wireless sensor and sensor networks
6) Network Security and Wireless Security
7) Advanced topics related to congestion control
8) Student Presentation (paper presentation and idea presentation) 

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

Reference books/materials:TBA

Grading scheme:
Paper and Idea Presentation 30 points
Project Report 30 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: COMP5712
Course title: Introduction to Combinatorial Optimization
Instructor: Sunil Arya
Room: 3514
Telephone: 2358-8769
WWW Page: http://www.cse.ust.hk/~arya

Area in which course can be counted: TH

Course description:
* An introduction to the basic tools of Combinatorial Optimization. 
* Includes: Linear Programming, Matching, Network Flow, Approximation Algorithms. 

Background needed: COMP3711 or equivalent + Linear Algebra

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.

Course outline/content (by major topics):


Reference books/materials:
Jiri Matousek and Bernd Gartner. Understanding and using linear programming, Springer, 2006. 
Vijay Vazirani. Approximation algorithms, Springer, 2001. 
David P. Williamson and David B. Shmoys. The design of approximation algorithms, Cambridge University Press, 2011 
Jon Kleinberg and Eva Tardos. Algorithm design, Pearson/Addison-Wesley, 2006. 

Grading scheme:
Homeworks, midterm and final examination. 

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Instructor Permission required

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
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 

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 Class Schedule & Quota (Spring 2017) for the timetable and quota.

Archive of past courses

Last modified by Bo Liu on 2017-1-10.