Fall 2025 CS Course Listings

This file contains the Fall 2025 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP 5211
Course title: Advanced Artificial Intelligence
Instructor: Prof. Fangzhen Lin
Office: 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:

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP 5212
Course title: Machine Learning
Instructor: Dr. May Fung
Office: 3539
Telephone: 2358-8332
Email:
WWW Page: https://mayrfung.github.io/teaching/comp5212f24

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

Course description:
This course covers core and recent machine learning algorithms. Topics include supervised learning algorithms (linear regression, logistic regression, generative models for classification, support vector machines), unsupervised learning (K-Means, mixture models, latent-variable models, expectation maximization), deep learning, deep generative models, and reinforcement learning (classic RL, deep RL). The course assumes students have a solid grasp of probabilities, linear algebra, and python programming. This course is math-intensive, assignments and final projects will require proficient probability/linear algebra foundations and programming skills.

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):
You may refer to https://jxhe.github.io/teaching/comp5212f24 for an example of covered topics.

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 4 Normal Assignments + 1 Programming Project: (50%)
o Attendance: (10%)
o Mid-term Examination: (20%)
o Final Examination: (20%)

Available for final year UG students to enroll: PG students have higher priority to enroll in this course. UG students will be considered only if there are spare quota.

Minimum CGA required for UG students: 3.6

Course code: COMP 5331
Course title: Knowledge Discovery in Databases
Instructor: Prof. Raymond Wong
Office: 3541
Telephone: 2358-6982
Email:
WWW Page: https://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. Classification.
2. Clustering.
3. Association.
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 (4th edition).

Reference books/materials:
Introduction to Data Mining. Pang-Ning Tan, Michael Steinbach, Anuj Karpatne and Vipin Kumar. Pearson Addison Wesley (2019).

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: 3.7

Course code: COMP 5411
Course title: Advanced Computer Graphics
Instructor: Prof. Pedro Sander
Room: 3504
Telephone: 2358-6983
Email:
WWW Page: https://cse.hkust.edu.hk/~psander/

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 covers advanced topics in computer graphics. More specifically, it covers modeling and processing geometric shapes, and rendering, lighting, and shading, using latest generation graphics hardware.

Exclusion(s): CSIT 5400

Background: COMP 3711, Linear Algebra, Calculus

Course outline/content (by major topics):
* Basics of Computer Graphics
* Graphics Processing Unit (GPU)
* Programmable Rendering Pipeline (Vertex, Geometry, and Pixel shaders)
* Surface lighting and shading
* Real-time shadow algorithms
* Global illumination
* Bezier and B-spline curves and surfaces
* Space-based and surface-based deformation
* Mesh simplification
* Mesh smoothing
* Future trends on GPU computing

Textbooks:
None.

Grading scheme:
Project: 48%
Exam: 45%
Participation: 7%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP 5621
Course title: Computer Networks
Instructor: Dr. Brahim Bensaou
Office: 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: COMP 4621

Course objective:
Upon completion of this course you will have an in depth knowledge about the foundations of current Internet applications, services 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: COMP 5711
Course title: Introduction to Advanced Algorithmic Techniques
Instructor: Prof. Ke Yi
Room: CYT-3002
Telephone: 2358-8770
Email:
WWW Page: https://cse.hkust.edu.hk/~yike/

Area in which course can be counted: Theoretical Computer Science (TH)

Course description:
This is an introductory graduate course on advanced algorithms. It covers most of the classical advanced topics in algorithm design, as well as some recent algorithmic developments, in particular algorithms for "big data".

Background: COMP 3711, 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):
* Randomized algorithms
* Amortized analysis
* Advanced data structures
* Fixed-parameter algorithms
* Approximation algorithms
* Sublinear-time algorithms
* Online algorithms
* Streaming algorithms
* External memory algorithms
* Parallel algorithms

Textbooks:
NIL

Reference books/materials:
[CLRS] Introduction to Algorithms (4th edition), by T. Cormen, C. Leiserson, R. Rivest, and C. Stein.
[KT] Algorithm Design, by Jon Kleinberg and Eva Tardos.
[MR] Randomized Algorithms, by Rajeev Motwani and Prabhakar Raghavan.
[CY] Small Summaries for Big Data, by Graham Cormode and Ke Yi.

Grading scheme:
Assignments: 20%
Midterm exam: 30%
Final exam: 50%

Available for final year UG students to enroll: Yes

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

Course code: COMP 6211J
Course title: Advanced Large-Scale Machine Learning Systems for Foundation Models
Instructor: Dr. Binhang Yuan
Office: 3517
Telephone: 6978
Email:
WWW Page: https://github.com/Relaxed-System-Lab/COMP6211J_Course_HKUST

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

Course description:
In recent years, foundation models have fundamentally revolutionized the state-of-the-art of artificial intelligence. Thus, the computation in the training or inference of the foundation model could be one of the most important workflows running on top of modern computer systems. This course unravels the secrets of the efficient deployment of such workflows from the system perspective. Specifically, we will (i) explain how a modern machine learning system (i.e., PyTorch) works; (ii) understand the performance bottleneck of machine learning computation over modern hardware (e.g., Nvidia GPUs); (iii) discuss four main parallel strategies in foundation model training (data-, pipeline-, tensor model-, optimizer-, MoE-, sequence- parallelism); and (iv) real-world deployment of foundation model including efficient inference and fine-tuning and RL alignment.

Exclusion(s): Only PG students from CSE are expected to take the course.

Course objective:
o Be able to illustrate basic concepts and principles in foundation models.
o Be able to describe main parallel training strategies for distributed training and inference.
o Be able to understand the system bottleneck of computations relevant to foundation models.
o Be able to deploy some foundation model workflow for PG-level research work.

Course outline/content (by major topics):
o Gradient Descent Algorithms & Automatic Differentiation
o Nvidia GPU Computation and Communication
o Large-Scale Pretrain Overview
o Data Parallel Training & Pipeline Parallel Training
o Tensor Model Parallel Training & Optimizer Parallel Training
o Sequence Parallel Training & Mixture of Expert Parallel Training
o Generative Inference Overview
o Disaggregation and Low-precision Compression for Latency-oriented Inference
o Batching and Offloading for Throughput-oriented Inference
o Student Presentations

Textbooks:
N/A

Reference books/materials: Course Website

Grading scheme: Literature Review Report + Presentation + Research Plan

Available for final year UG students to enroll: Not allowed

Minimum CGA required for UG students: N/A

Course code: COMP 6311G
Course title: Advanced Spatiotemporal Indexing and Query Processing
Instructor: Dr. Ziyi Liu
Office: 3208A
Email:

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

Course description:
This course introduces advanced techniques for indexing and querying large-scale spatiotemporal and graph-structured data. It covers spatial and temporal data models, and explores how various query types such as range, k-nearest neighbor, join, and shortest path are supported by suitable indexing methods. Topics include spatial indexes such as R-tree, KD-tree, and Octree, graph indexes such as Contraction Hierarchies and Pruned Landmark Labeling, as well as learning-based indexing for adaptive and efficient query processing. The course also addresses out-of- core and distributed indexing, with emphasis on algorithm design, performance optimization, and practical applications on real-world datasets. In addition to lectures, students will present and discuss recent research papers, fostering a deep understanding of current trends and open challenges in the field.

Course objective:
1) Understand key challenges in spatiotemporal data management and analytics.
2) Design and evaluate various index structures for spatiotemporal queries.
3) Implement and optimize query processing algorithms for different data types.
4) Apply indexing techniques to real-world datasets and analyze their effectiveness.

Course outline/content (by major topics):
Lec 1: Course Introduction
Lec 2: Introduction to Spatial Databases
Lec 3-4: Spatial Data Organisation (I)
Lec 5-6: Spatial Data Organisation (II)
Lec 7-8: Spatial Query Processing (Basic)
Lec 9-10: Spatial Query Processing (Advanced)
Lec 11: Quiz
Lec 12-13: Managing Spatiotemporal Data
Lec 14-15: Managing High Dimensional Data
Lec 16-17: Managing Multimedia Data
Lec 18-19: Advanced Topic: Route Planning in Road Network
Lec 20: Advanced Topic (Guest Lecture)
Lec 21: Trends and Course Review
Lec 22-23: Group Presentation

Reference books/materials:
Lecture Notes
Survey Papers
Research Papers

Grading scheme:
Participation: 10%
Assignments: 40%
Student Presentations: 50%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: N/A

Course code: COMP 6411D
Course title: Data Visualization
Instructor: Dr. Arpit Narechania
Room: 3552
Telephone: (852) 2358 6974
Email:
WWW Page: https://narechania.com/

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

Course description:
This is an introductory graduate course that teaches the design principles, considerations, and applications of data visualization, providing best practices and hands-on experience for designing, implementing, and critiquing visualizations across diverse domains for a variety of use-cases. No prior experience with computer programming is expected. The course will involve using data visualization systems as opposed to coding visualizations from scratch.

Background: Predominantly COMP students; others can also take the class, subject to quota availability and permission of the instructor.

Course objective:
In this course, students will:

  • Learn the fundamental principles of effective data visualization.
  • Understand the wide range of data visualization techniques and determine which visualizations are suitable for different types of data and objectives.
  • Learn how to design and implement data visualizations using both commercial and open-source software tools.
  • Discover how data visualization employs dynamic interaction methods to help users explore, analyze, and interpret data.
  • Acquire an understanding of human perceptual and cognitive capabilities as they relate to the design of effective data visualizations.
  • Develop the ability to critique different data visualization techniques based on user goals and objectives.

Course outline/content (by major topics):
The course will cover the theory and applications related to data visualization, including:
  • color and visual perception
  • user tasks such as analysis, sensemaking, and storytelling
  • visualization design guidelines
  • graphical integrity and misinformation
  • evaluating visualizations
  • cognitive biases in visualization
  • visualization of complex data types:
    • multivariate
    • tabular
    • graphs and networks
    • text and documents
    • geospatial
    • time series
  • WIMP and post-WIMP interactions (WIMP = windows, icons, menus, pointers)
  • visualizations across different displays and devices
  • accessibility in visualization
  • visual analytics
  • hands-on experience using open-source and commercial tools

Reference books/materials:
There are no required textbooks for this course. A free textbook that may help with learning the principles of web-based visualization development is “Interactive Data Visualization for the Web by Scott Murray”, O’Reilly Media, ISBN 9781449339739, accessible via O'Rielly or HKUST Library.

Grading scheme:
  • Homework: 40%
  • Exams: 30%
  • Group Project: 35%
  • Class Participation: 5%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP 6613F
Course title: Introduction to Quantum Information Processing
Instructor: Prof. Hans-Arno Jacobsen
Office: 3533
Email:

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

Course description:
This introductory course provides a foundational overview of quantum information processing, aimed at students with little or no prior background in quantum theory. Topics include basic principles of quantum mechanics relevant to computation, qubits and quantum gates, superposition, entanglement, and simple quantum algorithms. The course emphasizes the computer science perspective, exploring how quantum concepts translate into computational models and problem-solving approaches. Students will engage with selected contemporary literature, gaining exposure to emerging research directions such as quantum algorithms, quantum machine learning, and distributed quantum computing via student-led presentations. As a culminating activity, students will work individually or in small groups on a focused, research-oriented project—developing it from inception to realization and delivering a final presentation. By the end of the course, participants will understand core concepts, appreciate the potential and limitations of quantum computation, and be equipped to follow to some extent current developments in the field.

Background:
A Bachelor's degree in Computer Science, Mathematics or other Engineering

Course objective:
* Basic understanding of quantum information processing
* Be able to read some of the more Computer Science-related material on quantum information processing
* Coherently present research papers
* Execute a small-scale research project end-to-end (from inception, implementation to evaluation and presentation)

Course outline/content (by major topics):
* Nature of computation
* Computing with Circuits
* Quantum Bit (Qubit)
* Black box puzzle (towards quantum parallelism)
* First Gate, measuring qubits
* Single-qubit gates (H, X, Z, I)
* Superpositions
* Bloch Sphere (phase gates)
* Universal Gate Set
* Etc.

Reference books/materials:
To be determined

Grading scheme:
* Student engagement in class (via interaction and potential quizzes - 20%)
* Student presentation assessment (40%)
* Student project assessment (40%)

Available for final year UG students to enroll: No

Minimum CGA required for UG students: N/A

Please visit Class Schedule & Quota (Fall 2025) for the timetable and quota.

Archive of past courses

Last modified on 2025-08-26.