Spring 2015 CS Course Listings

This file contains the Spring 2015 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP5212
Course title: Machine Learning
Instructor: James Kwok
Room: 3519
Telephone: 2358-7013
Email:
WWW Page:

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:
Assignments/project (35%), Midterm (20%), Final (45%)

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A- (and permission of the instructor)

Course code: COMP5311
Course title: Database Architecture and Implementation
Instructor: Dimitris Papadias
Room: 3505
Telephone: 2358-6971
Email:
WWW Page:

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):
The instructor will teach the majority of the classes. Students will form groups. Each group will choose a general database area and prepare a presentation.

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: Permission of the instructor

Course code: COMP5421
Course title: Computer Vision
Instructor: Long Quan
Room: 3506
Telephone: 2358-7018
Email:
WWW Page: http://cse.hkust.edu.hk/~quan/comp5421/index.html

Area in which course can be counted: VG

Course description:
Introduction to modern computer vision fundamentals in visual descriptors, vision geometry, and object recognition.
Background: The equivalent prerequisites in linear algebra (eg. COMP3211 knowledge in linear algebra), in object-oriented programming (eg. COMP2012 object-oriented programming), algorithm design and analysis (eg. COMP171, COMP271) are required. Basic knowledge in image processing is helpful.

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

Course outline/content (by major topics):
1. Introduction
2. Visual features and descriptors (low level feature detection and description)
3. Vision geometry (mid-level geometry, projective geometry, cameras, and 3D reconstruction)
4. Visual recognition (high-level object recognition and image understanding)
5. Perspective

Textbooks:
Image-based Modeling, Long Quan, 2010.

Reference books/materials:
* Three-Dimensional Computer Vision, O. Faugeras, MIT Press, 1993
* The Geometry of Multiple Images, Faugeras, Luong, and Papadoupolo
* The Multi-View Geometry, Hartley and Zisserman
* Robot Vision, B.K.P. Horn, MIT Press, 1986
* Computer Vision, D. Ballard and C. Brown, Prentice-Hall, 1982
* Vision, David Marr, Freeman, 1982
* Computer Vision, A Modern Approach, D. Forsyth and J. Ponce
* Computer vision, Algorithms and Applications, Richard Szeliski, 2011

Grading scheme:
Project 1: 30% (individual or team up to two students)
Project 2: 30% (individual project)
Final Exam (written): 40%

Available for final year UG students to enroll: Yes.

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

The prerequisites: linear algebra, object-oriented programming, algorithm design and analysis

Course code: COMP5621
Course title: Computer Networks
Instructor: Lin Gu
Room: 3562
Telephone: 2358-6991
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: 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: 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.

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:
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: COMP6611A
Course title: Hot Topics in Data Center Networking and Cloud Computing
Instructor: Kai Chen
Room: 3546
Telephone: 2358-7028
Email:
WWW Page: (TBA)

Area in which course can be counted: NT

Course description:
Driven by technology advances and economic forces, data centers are being built around the world to serve as the infrastructures for many big data applications and cloud computing services. In this course, our goal is to study the critical technology trends and new challenges in data center networking and cloud computing. We will understand different trade-offs on performance, cost, scalability, manageability across the infrastructure, network, and application layers. The course will include student presentations, discussions, and a course project. The papers will be selected from top networking and system conferences.

Background: Networking

Course objective:
Understand the state-of-the-art research in data center networking and cloud computing

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

Textbooks:
No

Reference books/materials: (TBA)

Grading scheme:
There is no exams for this class. The course grade will be determined based on: paper review, in class paper presentation and debate, and a course project.

Available for final year UG students to enroll: Yes, this class is appropriate for graduate students and senior undergraduate students with background in networking.

Minimum CGA required for UG students: Instructor Permission required.

Please visit here for the timetable and quota.

Archive of past courses

Last modified by Yixian Zheng and Wenchao Wu on 2014-10-23.