Fall 2010 CS Course Listings

This file contains the Fall 2010 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP522
Course title: Machine Learning
Instructor: Dit-Yan Yeung
Room: 3541
Telephone: 2358-6977
Email:
WWW page: http://cse.hkust.edu.hk/~dyyeung/

Area in which course can be counted: AI

Course description: Machine learning is the science of making computer artifacts improve their performance without requiring humans to program their behavior explicitly. Machine learning has accomplished successes in a wide variety of challenging applications, ranging from computational molecular biology to computer vision to social web analysis. This course is a postgraduate-level introductory course in machine learning with emphasis put on the computational and mathematical principles underlying the most common machine learning problems and methods. It is not only suitable for students pursuing or planning to pursue research in machine learning or other related areas that focus on model and algorithm development, but is also suitable for students who want to apply disciplined machine learning techniques competently to their application-oriented research areas.

Prerequisite/Background:
Computer science: object-oriented programming and data structures, design and analysis of algorithms
Mathematics: multivariable calculus, linear algebra and matrix analysis, probability and statistics

Course objective:
By the end of this course, students are expected to demonstrate ample competence in the following:
- Ability to take a real-world application and formulate the learning problems involved in it by identifying the major learning-related issues
- Ability to choose and apply the most common methods available for each of the major learning problem types
- Ability to compare different machine learning methods according to common performance criteria
- Ability to design and conduct empirical studies in such a way that the experimental results can be interpreted in accordance with disciplined scientific and statistical principles
- Ability to understand the motivations behind and the key issues studied in some recent research topics in machine learning

Course outline/content (by major topics):
** TENTATIVE TOPICS **
Supervised learning
Bayesian decision theory
Parameter estimation
Multivariate methods
Dimensionality reduction
Clustering
Nonparametric methods
Decision trees
Linear discrimination
Multilayer perceptrons
Support vector machines and kernel methods
Bayesian methods
Performance evaluation and comparison
Ensemble learning
Reinforcement learning
Quick overview of selected recent topics

Textbooks:
Ethem Alpaydin (2010). Introduction to Machine Learning. Second Edition. MIT Press.

Reference books/materials:
1. Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer.
2. Richard O. Duda, Peter E. Hart, and David G. Stork (2001). Pattern Classification. Second Edition. Wiley.
3. Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009). The Elements of Statistical Learning. Second Edition. Springer.
4. Tom M. Mitchell (1997). Machine Learning. McGraw-Hill.
5. Other assigned reading material.

Grading scheme:
Class participation (10%)
Problem sets (20%)
Programming projects (20%)
Midterm exam (20%)
Final exam (30%)

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A-

Course code: COMP530
Course title: Database Architecture and Implementation
Instructor: Wilfred Ng
Room: 3505
Telephone: 2358-6979
Email:
WWW page: http://cse.hkust.edu.hk/faculty/wilfred/

Area in which course can be counted: DB

Course description:
This course introduces basic 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.

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.

Course outline/content (by major topics):


Textbooks:
Textbook: Database System Concepts, 5th Edition. A. Silberschatz, H. Korth, and S. Sudarshan.

Reference books/materials:
Reference: Database Management Systems, 3rd Edition. Raghu Ramakrishnan and Johannes Gehrke.

Grading scheme:
Student Presentations 20%, Midterm 35%, Final 45%. Each presentation should be around 40 minutes. All students in each group should participate.

Available for final year UG students to enroll: No.

Minimum CGA required for UG students: (can be "permission of the instructor")

Background needed: COMP252

Course code: COMP537
Course title: Knowledge Discovery in Databases
Instructor: Raymond Chi-Wing Wong
Room: 3542
Telephone: 23586982
Email:
WWW page: http://cse.hkust.edu.hk/~raywong/

Area in which course can be counted: DB/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.

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 and Micheline Kamber
Morgan Kaufmann Publishers (2nd edition)

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

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

Background needed: COMP271

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

Minimum CGA required for UG students: none

Course code: COMP541
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/comp541

Area in which course can be counted: VG

Course description:
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.

Background: COMP271, Linear Algebra, Calculus

Exclusion: CSIT540

Course outline/content (by major topics):
Basics of Computer Graphics
Curves and surfaces (Bezier, b-spline, implicit surfaces)
Mathematical: least squares, PCA, SVD
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

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

Reference books/materials:

Grading scheme:
70% - Four programming projects
30% - Final exam

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: permission of the instructor

Course code: COMP561
Course title: Computer Networks
Instructor: Lin Gu
Room: 3562
Telephone: 23586991
Email:
WWW page:

Area in which course can be counted: NT

Course description: We will cover advanced topics in emerging computer networking technologies, including wireless networks, peer-to-peer networks, sensor networks, datacenter networking, wide-area networks and local area networks, multimedia networking, and network security. Lecture materials will be drawn from the text books, conference proceedings, readings, and other sources. Students are expected to read the material in advance, present papers, participate in discussions, and develop projects in groups.

Exclusion: COMP362

Course outline/content (by major topics):
The following list gives the approximate order that topics will be covered in COMP561, fall of 2010. Changes/additions will be made as the semester progresses. In addition to the textbook, notes and research papers will be used.

Computer Networks and the Internet
Application Layer
Transport Layer
Network Layer and Routing
Link Layer and Local Area Networks
Cloud computing
Multimedia Networking
Internet Security
Peer-to-peer networks
Sensor network
Mobile Communication and computing
Datacenter networking

Textbooks:
Computer Networking: A Top-Down Approach (5th Edition), James F. Kurose and Keith W. Ross (http://www.aw-bc.com/kurose-ross/)

Reference books/materials:
Computer Networks :A Systems Approach (4th Edition), Larry Peterson and Bruce S. Davie
W. Richard Stevens, UNIX Network Programming Vol. 1, 3rd Ed. Addison-Wesley, 2003.
In addition, a collection of papers from journals, conference proceedings, and web sites will be studied.

Grading scheme:
Homework 15 points
Presentation 15 points
Projects 35 points
Final Examination 35 points

Presentation:
I will distribute a list of papers from major conferences and journals. Each project group (consisting of one or two students) will give a 30 minute presentation on one of the papers they select.

Project:
Students can choose a project from a list that I will distribute, or propose a project idea to the instructor. Every student is required to submit a short project proposal (1-2 pages), code, and a final project report. Structure the final project report as a research paper: title, abstract, introduction and motivation, related works, approach description, experimental methodology and results, conclusions, and references. The paper should be single space, double column, and using 10-pt fonts. We expect that the project report has no less than 10 pages of technical content. Students are encouraged to demonstrate their project implementation.

NOTES:
The instructor reserves the right to modify course policies, the course calendar, and assignment specifications. Unless otherwise stated, all work submitted by you should be your own. Copying of assignments, help taken or given in programming or sharing of algorithms and results would constitute cheating. If there is any doubt about the appropriateness of your actions, please contact the instructor for explicit clarification. Cheating is an offense and will result in disciplinary actions against those involved. Make-ups for examinations may be arranged if your absence is caused by medical or personal emergency with written evidence. A written explanation (including supporting documents) must be submitted to the instructor; if the explanation is acceptable, an alternative to the examination may be arranged. When it is possible, make-up arrangements must be requested in advance.

Course code: COMP570
Course title: Introduction to Advanced Algorithmic Techniques
Instructor: Sunil Arya
Room: 3509
Telephone: 2358-8769
Email:
WWW page: http://cse.hkust.edu.hk/~arya/

Area in which course can be counted: TH

Course description:This is an introductory graduate course in algorithmic techniques. Topics include: advanced data structures; graph algorithms; amortization; approximation algorithms; on-line algorithms; randomized and probabilistic analysis.

Background: COMP271, COMP272

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

Course outline/content (by major topics):
Network Flow
Approximation Algorithms
Randomized Algorithms

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

Reference books/materials:
- Randomized Algorithms. Rajeev Motwani, Prabhakar Raghavan, Cambridge University Press, 1995.
- Introduction to Algorithms (2nd Edition). T. Cormen, C. Leiserson, R. Rivest, C. Stein. McGraw Hill and MIT Press.
- Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Mitzenmacher and E. Upfal. Cambridge University Press, 2005.

Grading scheme: Based on homeworks and exams.

Available for final year UG students to enroll: Yes

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

Course code: COMP670S
Course title: Topics in TH: Data Stream Algorithms
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 course studies data stream algorithms, namely, algorithms that solve a problem by making one pass over the data set while using small memory. These algorithms are important in many application areas such as databases and networking, where data arrives at a high speed and there is no time and/or need to store it for offline processing. We will start with the classical work in this area, followed by a potpourri of recent developments.

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

Course outline/content (by major topics):
* Data stream model
* Distinct count
* Count sketch; count-min sketch; heavy hitters
* Frequency moments; norms
* Quantiles
* Sliding windows
* Random sampling
* Graph problems: Spanners; shortest paths; triangle counting; minimum spanning trees
* Geometric problems: eps-approximations; coresets
* Distributed streams
* Lower bounds

Textbooks: none

Reference books / materials: Lecture notes and research papers
Grading scheme: homework (30%), midterm exam (40%), paper presentation (30%)

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: permission of the instructor

Course code: COMP680F
Course title: Topics in CE: Wireless Sensor Networks
Instructor: Dr. Yunhao Liu
Room: 3548
Telephone: 2358-7019
Email:

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

Course description:
This is a seminar-style research-oriented course. The course will cover new advances in wireless sensor networks. Students should have sufficient knowledge in computer systems, operating systems, networking, and wireless communications. Students require the instructor's approval before taking the course.

Course objective: Research on wireless sensor networks (WSNs) has recently received a great deal of attention world wide. This course will begin with the background and motivation of WSNs. The course will cover from low-level sensor node design to high-level applications. The focus of this course will be on new challenging research issues in this exciting research area.

Grading scheme:
Based on projects and class presentation and discussion participation.

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 Derek Hao Hu on 2010/06/23.