Fall 2013 CS Course Listings

This file contains the Fall 2013 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:
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):

Textbooks:
Supervised learning
Bayesian decision theory
Parameter estimation
Dimensionality reduction
Clustering
Nonparametric methods
Decision trees
Linear discrimination
Multilayer perceptrons
Support vector machines
Performance evaluation and comparison
Ensemble learning
Recent topics

Reference books/materials:
1. Kevin P. Murphy (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
2. Ethem Alpaydin (2010). Introduction to Machine Learning. Second Edition. MIT Press.
3. Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009). The Elements of Statistical Learning. Second Edition. Springer.
4. Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer.
5. Richard O. Duda, Peter E. Hart, and David G. Stork (2001). Pattern Classification. Second Edition. Wiley.
6. Tom M. Mitchell (1997). Machine Learning. McGraw-Hill.
7. 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- (and permission of the instructor)

Course code: COMP5221
Course title: Natural Language Processing
Instructor: Dekai Wu
Room: 3539
Telephone: 2358-6989
Email:
WWW Page: (TBA)

Area in which course can be counted: AI

Course description:
Techniques for parsing, interpretation, context modeling, plan recognition, generation. Emphasis on statistical approaches, neuropsychological and linguistic constraints, large text corpora. Applications include machine translation, dialogue systems, cognitive modeling, and knowledge acquisition.
Exclusion: COMP4221
Background: COMP 3211

Course objective:
(TBA)

Course outline/content (by major topics):
(TBA)

Textbooks:
(TBA)

Reference books/materials:
(TBA)

Grading scheme:
30% - 3 to 4 assignments
5% - participation (in class and on forum)
15% - midterm
20% - final
30% - project

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of instructor

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.
Background needed: 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: Brahim Bensaou
Room: 3537
Telephone: 23587014
Email:
WWW Page: http://course.cse.ust.hk/comp5621/

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: 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 (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: COMP5631
Course title: Cryptography and Security
Instructor: Prof. Cunsheng Ding
Room: 3518
Telephone: 2358 7021
Email:
WWW Page: http://cse.hkust.edu.hk/faculty/cding/COMP581/

Area in which course can be counted: Software and Applications

Course description:
This course gives an in depth coverage of the theory and applications
of cryptography, and system security. In the part about cryptography,
basic tools for building security systems are introduced. The system
security part includes electronic mail security, IP security, Web
security, and firemalls.

Course objective:
After completion of this course, students will display a breadth of
knowledge of both the principles and practice of cryptography and
systems security, and master basic tools for building security systems.

Course outline/content (by major topics):
History of cryptography, classical ciphers, design and analysis of
block ciphers and stream ciphers, public-key cryptography, hash
functions, digital signature, group signature, proxy signature,
user and data authentication, data integrity, nonrepudiation,
Key management, public key infrastructure, cryptographic protocols,
email security, web security, network security, distributed systems
security

Textbooks:
No textbook, but lecture slides will be posted online.

Reference books/materials:
W. Stallings, Cryptography Theory and Network Security,
Fourth/Fifth Edition, Pearson Education.

Grading scheme:
Assignments, course project, midterm and final examination.

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: A-

Course code: COMP5712
Course title: Introduction to Combinatorial Optimization
Instructor: Sunil Arya
Room: 3514
Telephone: 23588769
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.
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):

Textbooks:

Reference books/materials:

Reference books:
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: permission of the instructor

Course code: COMP6311D
Course title: Hot Topics on Big Data: Algorithms, Analytics and Applications
Instructor: Lei Chen and Ke Yi
Room: 3509/3552
Telephone: 852-23586980
Email: ,
WWW Page:

Area in which course can be counted: DB

Course description:
In this course, we will mainly discuss the topics related Big Data, especially on algorithms, analytics and applications. The course will cover the state-of-the-art solutions in this area and present some interesting research topics for students to work on.

Course objective:
To gain deep understanding about the concept of Big Data, especially on how to store, transmit, query, mining, and visualizing Big Data. The students will be trained to survey, analyze and criticize research papers, obtain hands-on experience on Big Data related research projects.

Course outline/content (by major topics):
1. Big Data Overview
2. Big Data Algorithms
3. Big Data Analytics
4. Big Data Applications

Textbooks:

Reference books/materials:
There will be no textbook or reference book. The course material will be based mostly on recent SIGKDD, ICDM, SIGMOD, VLDB and ICDE papers.

Grading scheme:
* Student presentations: 30%
* Survey and Project implementation: 60%
* Participation and activity in class: 10%

Available for final year UG students to enroll: No

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

Please visit your Student Center for the timetable and quota.

Archive of past courses

Last modified by Zhiyang Su on 2013-08-29.