Fall 2012 CS Course Listings

This file contains the Fall 2012 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP5212
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 principled machine learning techniques competently to their application-oriented research areas.

Background needed:
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 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):
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

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:
Problem sets (20%)
Programming projects (20%)
Midterm exam (20%)
Final exam (40%)

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A-

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: Raymond Chi-Wing Wong
Room: 3542
Telephone: 2358-6982
Email:
WWW page: http://cse.hkust.edu.hk/~raywong/

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.

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%

Background needed: COMP271

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:
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: COMP3711, Linear Algebra, Calculus

Exclusion: CSIT5400

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

Text book:
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: Brahim Bensaou
Room:
Telephone: 23587014
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; TCP congestion control, quality of service, emerging trends in networking.

Exclusion(s): COMP 4622

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.

Textbooks:
   *James Kurose and Keith Ross, Computer Networking: A Top Down Approach, (5th Ed.), Pearson, 2009.
   *A collection of papers and articles provided as a reading list.

Grading scheme:
   *Homework (can be a paper presentation), Mid-term and Final Exam.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Instructor Permission required

Course code: COMP5712
Course title: Introduction to Combinatorial Optimization
Instructor: Sunil Arya
Room: 3514
Telephone: 2358-8769
Email:
WWW page: http://cse.hkust.edu.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.

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.

Textbooks:
   * Jiri Matousek and Bernd Gartner. Understanding and using linear programming, Springer, 2006.
   * William J. Cook, William H. Cunningham, William R. Pulleyblank, and Alexander Schrijver. Combinatorial optimization, John Wiley & Sons, 1998.
   * 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.

Background needed:
   * COMP3711 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: COMP6111B
Course title: Introduction to Parallel Programming on the GPU
Instructor: Qiong Luo
Room: 3554
Telephone: 2358-6995
Email:
WWW page:

Area in which course can be counted: Software and Applications

Course description:
This course introduces modern GPUs (Graphics Processing Units) as parallel computing platforms and teaches parallel programming on the GPU for general-purpose computing applications.

Course outline/content (by major topics):
1. Overview of parallel computing architectures
2. Overview of parallel programming models
3. Modern GPU Architecture
4. The CUDA Programming Framework
5. GPGPU Applications

Textbooks: None

Reference books/materials:
1. The CUDA programming guide
2. A collection of recent papers

Grading scheme: 50% presentation, and 50% project.

Pre-requisites/Background needed: None

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP6311B
Course title: Topics in DB: Managing Probabilistic Data in Advanced Database Applications
Instructor: Wilfred Ng
Room: 3505
Telephone: 2358-6979
Email:
WWW page:

* Instructor's approval is needed for registration

Area in which course can be counted: DB

Course description:
As probabilistic data continues to populate in a wide spectrum of new database applications such as RFID and genetic applications, we are facing new challenges that require us to relax many traditional database assumptions and advance existing querying and mining techniques. In addition to relaxing conventional constraints such as functional dependencies and ranking, we study new modeling, querying and mining techniques in many database applications. For example, modeling RFID data and microarraying data are needed to support mining more interesting and useful probabilistic information. Information extracted from independent web sources may also require effective techniques of merging data in a probabilistic manner. In this course, we will discuss in depth the research issues arising from probabilistic data management and its applications.

Exclusion/Background needed: Excellent fundamental database knowledge and basic research techniques.

Course objective:
To gain a better understanding of the current research topics in advanced probabilistic databases and the applications, especially how to store, query, mine probabilistic data in sequence databases, RFID databases, web data, and bioinformatics data. To equip you with the techniques to survey, analyze and criticize research papers, obtain hands-on experience on database projects and participate research with other students.

Course outline/content (by major topics):
1. Basics probabilistic data models.
2. Ranking in probabilistic databases.
3. Data Dependencies in probabilistic relational databases.
4. Mining probabilistic sequence databases.
5. Probabilistic micro arraying data mining.
6. Probabilistic convex hull query.
7. Probabilistic RFID Data modelling.
8. Probabilistic Data and Web searching and mining.

Textbooks: No need.

Reference books/materials:
   * Database journals such as IEEE TKDE, ACM TODS and Information Systems.
   * Conference Proceedings such as SIGMOD, VLDB, ICDE, PODS, EDBT, KDD, ICDM.
   * Electronic Resources:
     1. ACM Digital Library: http://portal.acm.org/dl.cfm
     2. CiteSeer Computer Science: http://citeseer.ist.psu.edu/
     3. ACM Special Interest Group on Management of Data (SIGMOD): http://www.acm.org/sigmod
     4. ACM Special Interest Group on Information Retrieval (SIGIR): http://www.acm.org/sigir
     5. Very Large Data Bases (VLDB): http://www.vldb.org/
     6. DBLP: http://www.informatik.uni-trier.de/~ley/db/index.html
     7. IEEE Computer Society: http://www.computer.org/
     8. IEEE Explore: http://ieeexplore.ieee.org/

Grading scheme:
No Final examination. Paper Presentation (30%), Survey Assignment (30%), Individual Research Report (40%).

Available for final year UG students to enroll: Subject to instructor's approval.

Minimum CGA required for UG students: A or above.

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 Yongxin Tong on 2012/08/23.