Fall 2022 CS Course Listings

This file contains the Fall 2022 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: https://cse.hkust.edu.hk/~flin/

Area in which course can be counted: Artificial Intelligence (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 genetic 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:
NIL

Grading scheme: 40% for assignments and projects, 60% for the final exam.

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: Dr. Minhao Cheng
Room: 2542
Telephone: 2358-7011
Email:
WWW Page: https://cse.hkust.edu.hk/~minhaocheng/

Area in which course can be counted: Artificial Intelligence (AI)

Course description:
This course covers some classical and advanced algorithms in machine learning. Topics include: Linear models (linear/logistic regression, support vector machines), Non-linear models (tree-based methods, kernel methods, neural networks), learning theory (hypothesis space, bias/variance tradeoffs, VC dimensions). The course will also discuss some advanced topics of machine learning such as testing-time integrity in trustworthy machine learning and neural architecture search in AutoML.

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:
Upon successful completion of the proposed course, students will be able to:
* Gain an overview of Machine Learning as a subject of study;
* Gain an understanding of the fundamental issues and principles in machine learning;
* Gain an understanding of core and recent machine learning algorithms;
* Gain an ability to apply core and recent machine learning algorithms to solve real-world problems.

Course outline/content (by major topics):
* Introduction to Machine Learning
* Math basics: Linear algebra, Calculus, Probability
* Fundamentals:
    * Linear models: linear regression, logistic regression and support vector machine
    * Optimization ( gradient descent, stochastic gradient descent and its variants)
    * Clustering, principle component analysis
    * Learning theory
* Recent topics in Machine Learning: (This part will not be included in the final exam)
    * AutoML
    * Trustworthy Machine Learning

Textbooks:
NIL

Reference books/materials:
o Andrew Ng. Lecture Notes on Machine Learning. Stanford. https://cs229.stanford.edu/syllabus.html
o I Goodfellow, Y Bengio, A Courville (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/

Workload and Grading:
o 3 Written Assignments: (3x8%)
o 2 Hands-on Assignments (2x8%)
o Term Project: (35%)
o Final examination: (25%)

Available for final year UG students to enroll: No

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

Course code: COMP5331
Course title: Knowledge Discovery in Databases
Instructor: Prof. Raymond Wong
Room: 3541
Telephone: 2358-6982
Email:
WWW Page: http://cse.hkust.edu.hk/~raywong/

Area in which course can be counted: Data, Knowledge and Information Management (DB) or Artificial Intelligence (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.

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 Prof. Pedro Sander
Room: 3515 (Prof. Chiew-Lan Tai); 3504 (Prof. Pedro Sander)
Telephone: 2358-7020 (Prof. Chiew-Lan Tai); 2358-6983 (Prof. Pedro Sander)
Email: ,
WWW Page: https://cse.hkust.edu.hk/~taicl/ (for Prof. Chiew-Lan Tai) and https://cse.hkust.edu.hk/~psander/ (for Prof. Pedro Sander)

Area in which course can be counted: Vision and Graphics (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): CSIT5400

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)

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: https://cse.hkust.edu.hk/~csbb/

Area in which course can be counted: Networking and Computer Systems (NE)

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: 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:
NIL

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: COMP5711
Course title: Introduction to Advanced Algorithmic Techniques
Instructor: Dr. Amir Goharshady
Room: IAS 2003
Telephone: 2358-8339
Email:
WWW Page: https://cse.hkust.edu.hk/~goharshady/

Area in which course can be counted: Theoretical Computer Science (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):
* Amortized Analysis
* Randomized Algorithms
* Randomized Complexity Classes
* Parameterized Algorithms
* Exponential-Time Algorithms
* Kernelization
* Approximation Algorithms
* Streaming Algorithms
* 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: 40%
Final exam: 60%

Available for final year UG students to enroll: Yes

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

Course code: 6211E
Course title: Optimization for Machine Learning
Instructor: Prof. Tong Zhang
Room: 3455
Telephone: 3469-2681
Email:
WWW Page: http://tongzhang-ml.org/

Area in which course can be counted: Artificial Intelligence (AI)

Course description:
This course covers modern optimization methods for machine learning applications. We will discuss both theory and implementation of common optimization algorithms in the context of machine learning applications. The topics covered include convex optimization, stochastic optimization, nonconvex optimization, and distributed optimization.

Background:
Basic Machine Learning, Linear Algebra, Calculus and Probability

Course objective:
The students are expected to learn state of the art optimization techniques commonly used in machine learning, and how to apply these methods to various applications.

Course outline/content (by major topics):
NIL

Reference books/materials:
* Convex Optimization. Stephen Boyd and Lieven Vandenberghe, Cambridge University Press.
* Convex Optimization: Algorithms and Complexity by Sébastien Bubeck.

Grading scheme:
* Theory and programming assignments
* In class midterm exam
* Takehome Final Exam and Project

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: 3.8

Course code: 6411B
Course title: Advanced Topics in 2D and 3D Deep Visual Scene Understanding
Instructor: Dr. Dan Xu
Room: 3505
Telephone: 2358-8837
Email:
WWW Page: https://www.danxurgb.net/

Area in which course can be counted: Vision and Graphics (VG)

Course description:
Visual scene understanding is an important and fundamental field for advanced application scenarios such as autonomous driving and robotics. This course majorly focuses on delivering deep learning-based visual scene understanding techniques in both 2D and 3D perspectives. In the 2D part, it introduces topics including image and scene classification, semantic segmentation, and object detection. In the 3D part, it shows how 3D scene understanding can be performed through learning from 2D inputs, involving topics such as scene depth estimation, camera pose prediction, 3D scene reconstruction, and visual SLAM. Several representative deep scene understanding architectures and frameworks in supervised or self-supervised settings together with the 2D/3D tasks are also presented in the course.

Background: Instructor's approval is required for undergrad students to register the course for credits. There are no strict prerequisites for this course. But basic knowledge about computer vision and deep learning fundamentals are beneficial and necessary.

Course objective:
The objectives of the course are to help the students:
(i) obtain basic knowledge of visual scene understanding techniques for various intelligent applications in autonomous driving and robotics.
(ii) learn fundamentals in deep learning-based architectures/frameworks for several important 2D and 3D visual scene understanding tasks.
(iii) gain a sense of current research and development trends in academia and industry in the domain of visual scene understanding.
(iv) learn skills of digging into and presenting for research papers published at top conferences/journals.

Course outline/content (by major topics):
Basic deep network architecture and learning, common 2D and 3D computer-vision-based scene understanding tasks including scene depth estimation, 2D/3D object detection, semantic segmentation, 3D scene reconstruction, visual SLAM etc., and the visual scene understanding techniques for real-world large-scale application scenarios such as autonomous driving and robotics.

Reference books/materials:
* Multi-View Geometry in Computer Vision, Richard Hartley and Andrew Zisserman, Cambridge University Press, 2004.
* Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016.
* Computer Vision: Algorithms and Applications, Richard Szeliski, Springer.

Grading scheme:
Paper review 25%, In-class presentation and discussion 25%, Final project 50%.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: 3.7 and permission of the instructor

Please visit Class Schedule & Quota (Fall 2022) for the timetable and quota.

Archive of past courses

Last modified on 2022-10-11.