Fall 2018 CS Course Listings

This file contains the Fall 2018 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP5211
Course title: Advanced Artificial Intelligence
Instructor: Prof. Fangzhen Lin
Room: 3557
Telephone: 2358-6975
WWW Page: http://www.cse.ust.hk/~flin/

Area in which course can be counted: AI

Course description:
This advanced AI course will cover the main concepts and techniques in AI. The major topics will be: AI agents, problem solving, machine learning, knowledge and reasoning, uncertain knowledge and reasoning.

Course objective:
Students are expected to gain deep understanding of key concepts and techniques in AI, including heuristic search strategies for single agent problem solving as well as multi-agent strategic planning such as in game playing, knowledge representation and reasoning using both logic and probabilities, machine learning, and integrated agent design.

Course outline/content (by major topics):
2. Agents: production systems, learning with perceptrons (linear networks), and genertic programming.
3. Game theory.
4. AI search.
5. Knowledge representation and reasoning: logic and belief networks.
6. Machine learning.

Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach Prentice Hall, 2003.
Reference books/materials:

Grading scheme:
Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP5212
Course title: Machine Learning
Instructor: Prof. Dit-Yan Yeung
Room: 3531
Telephone: 2358-7009
WWW Page: http://www.cse.ust.hk/faculty/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.

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 experiment results can be interpreted in accordance with disciplined scientific and statistical principles.

Course outline/content (by major topics):
Bayesian decision theory
Parameter estimation for generative models
Linear and logistic regression
Feedforward neural networks
Support vector machines
Model assessment and selection
Deep learning models
Recurrent neural networks
Clustering and mixture models
Nearest neighbor classifiers
Decision trees
Dimensionality reduction
Ensemble learning
Matrix factorization
Probabilistic graphical models
Probabilistic topic models
Hidden Markov models
State space models
Reinforcement learning
Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach Prentice Hall, 2003.
Reference books/materials: * Kevin P. Murphy (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
* Ethem Alpaydin (2014). Introduction to Machine Learning. Third Edition. MIT Press.
* Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). Deep Learning. MIT Press.
* Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer.
* Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009). The Elements of Statistical Learning. Second Edition. Springer.
* Other assigned reading materials

Grading scheme: Problem set (20%)
Programming assignments (15%)
Programming project (15%)
Final exam (50%)

Available for final year UG students to enroll: No

Minimum CGA required for UG students: N/A

Course code: COMP5331
Course title: Knowledge Discovery in Databases
Instructor: Dr. Raymond Wong
Room: 3541
Telephone: 2358-6982
WWW Page:
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: COMP 271

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):
2. Clustering.
3. Classification.
4. Data Warehouse.
5. Data Mining over Data Streams.
6. Web Databases.
7. Multi-criteria Decision Making.

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: Prof. Chiew-Lan Tai and Dr. Pedro Sander
Room: 3515 (Prof Chiew-Lan Tai); 3504 (Dr. Pedro Sander)
Telephone: 2358-7020 (Prof Chiew-Lan Tai); 2358-6983 (Dr. Pedro Sander)
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 consists of two parts. The first part covers advanced topics in modeling and processing geometric shapes, and the second part covers topics on geometry rendering, lighting, and shading, using latest generation graphics hardware.
Exclusion(s): CSIT 5400

Background: COMP3711, Linear Algebra, Calculus

Course outline/content (by major topics):
* Basics of Computer Graphics
* Bezier and B-spline curves and surfaces
* Space-based and surface-based deformation
* Mesh simplification
* Mesh 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

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

Reference books/materials:
Grading scheme: Part 1 (Geometry) : Written Homework(20%), Programming Assignment (20%), Exam (60%)
Part 2 (Rendering) : Programming assignments (40%), Exam (60%)

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: Dr. Brahim Bensaou
Room: 3537
Telephone: 2358 7014
Email: WWW Page: http://www.cse.ust.hk/~csbb

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; wireless and mobile networking, multimedia networking.

Exclusion: 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.

Course outline/content (by major topics):

*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, paper presentation, 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: 2533
Telephone: 2358-7021
WWW Page: http://www.cse.ust.hk/faculty/cding/

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, VPNs, and firewalls.

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, VPNs, distributed systems security.

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: COMP5711
Course title: Introduction to Advanced Algorithmic Techniques
Instructor: Dr. Ke Yi
Room: 3547
Telephone: 2358-8770
WWW Page: http://www.cse.ust.hk/~yike

Area in which course can be counted: TH

Course description:
This is an introductory graduate course in algorithmic techniques.

Background: COMP3711, Discrete Mathematics, Probability

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

Course outline/content (by major topics):
Fixed-parameter algorithms
Approximation algorithms
Local search
Amortized analysis
Randomized algorithms
Streaming algorithms
External-memory algorithms
Parallel and distributed algorithms

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

Reference books/materials:
* Introduction to Algorithms (3rd Edition). T. Cormen, C. Leiserson, R. Rivest, C. Stein. McGraw Hill and MIT Press.
* Randomized Algorithms. Rajeev Motwani, Prabhakar Raghavan, Cambridge University Press, 1995.
* Algorithm Design. Jon Kleinberg and Eva Tardos, Addison Wesley, 2005.

Grading scheme:
Assignments: 10%
Midterm exam: 30%
Final exam: 60%

Available for final year UG students to enroll: Yes

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

Please visit Class Schedule & Quota (Fall 2018) for the timetable and quota.

Archive of past courses

Last modified by Xuanwu Yue on 2018-08-14.