Fall 2019 CS Course Listings

This file contains the Fall 2019 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
Email:
WWW Page: http://cse.hkust.edu.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):
1.Introduction.
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.

Textbooks:
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: COMP5222
Course title: Statistical Learning Models for Text and Graph Data
Instructor: Dr. Yangqiu Song
Room: 3518
Telephone: 2358-6987
Email:
WWW Page: http://cse.hkust.edu.hk/~yqsong/

Area in which course can be counted: AI

Course description:
This course will introduce a number of important statistical methods and modeling principles for analyzing large-scale data sets, with a focus on complex data structures such as text and graph data. Topics covered include sequential models, structure prediction models, deep learning attention models, etc., as well as open research problems in this area. This course is co-listed with MATH 5471.

Background:
Students are expected to have probability, linear algebra, and machine learning background. Students should have taken programming courses.

Course outline/content (by major topics): Statistical learning will be an integral part of postgraduate student training. Text and graph data are emerging data structures that are useful in many practical applications. Knowing how to deal with them will benefit a lot of students for their future career. This course will introduce basic and advanced statistical learning models, algorithms, and applications of text and graph data. It will provide a comprehensive set of knowledge to deal with real data analytics problems in the era of big data.
* Topics
* Introduction
* Sequence Modeling
* Featurized Sequence Modeling
* Neural Sequence Modeling
* SGD Optimization
* Word Embedding
* Topic Model
* Graph Model
* Sequence Tagging
* Constraint Models
* Knowledge Graphs

Grading scheme:
Letter grades from F to A+
20% homework and paper reading
40% projects
40% final exam

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

Minimum CGA required for UG students: 3.7

Course code: COMP5331
Course title: Knowledge Discovery in Databases
Instructor: Dr. Raymond Wong
Room: 3541
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: COMP 3711

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. Graph 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: 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

Textbooks:
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://cse.hkust.edu.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):

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, 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
Email:
WWW Page: http://cse.hkust.edu.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.

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: COMP5711
Course title: Introduction to Advanced Algorithmic Techniques
Instructor: Dr. Ke Yi
Room: 3547
Telephone: 2358-8770
Email:
WWW Page: http://cse.hkust.edu.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

Textbooks:
Nil.

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.

Course code: COMP6211D co-listed with ELEC6910T
Course title: Special Topcis in Deep Learning
Instructor: Dr. Qifeng Chen
Room: 3508
Telephone: 2358-8838
Email:
WWW Page: http://cqf.io

Area in which course can be counted: AI

Course description:
This course focuses on deep learning and its applications in various areas. The topics include the basis of deep learning and its applications in computer vision, 3D vision, sequential modelling, speech processing, graph processing, and generative models. Specifically, various forms of deep neural networks will be introduced, such as convolutional neural networks, context aggregation networks, recurrent neural networks, graph neural networks, and generative adversarial networks. The students have opportunities to implement deep learning models for some AI tasks such as image understanding, image synthesis, and speech enhancement.

Background: Students are expected to have probability, linear algebra, and machine learning background. Students should have taken programming courses.

Course objective:
The course aims at introducing state-of-the-art deep learning models and their applications in various areas. After taking this course, students should have a broad knowledge of deep learning and its applications in computer vision, sequential modelling, and signal processing. The students should be able to develop AI applications for solving real-world problems.

Course outline/content (by major topics):
Week 1-2: Overview of Deep Learning: Architecture, Losses, and Optimization
Week 3-4: Convolutional Neural Networks: Dilated Convolutions, ResNet, Perceptual losses
Week 5: Deep 3D Vision: PointNet++, OctNet, Tangent convolutions
Week 6-7: Graph Convolutional Networks for Graph Processing and Optimization
Week 8-9: Sequential Modelling and Signal Processing: RNN, LSTM, TCN, and WaveNet
Week 10-11: Generative Models: GAN, Pix2pix, CycleGAN, CRN ,VAE
Week 12-13: Final project presentation and project report due

Reference books/materials:
Ian J. Goodfellow, Yoshua Bengio, Aaron C. Courville. Deep Learning. Adaptive computation and machine learning, MIT Press 2016
Chritopher Bishop, Pattern Recognition and Machine Learning, Published by Springer, 2007.

Grading scheme:
Class Participation: 10%
In-class presentation: 15%
Homework: 30%
Final project: 45%

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

Minimum CGA required for UG students: N/A

Please visit Class Schedule & Quota (Fall 2019) for the timetable and quota.

Archive of past courses

Last modified by Xinchen Wan on 2019-08-05.