Fall 2023 CS Course Listings

This file contains the Fall 2023 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
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:

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. Minhao Cheng
Room: CYT-3004
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
* Advanced methods:
    * Kernel methods
    * Tree-based method
    * Deep Feedforward Networks
    * Convolutional Neural Networks
    * Recurrent Neural Networks
    * Transformer and BERT
* 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: COMP 5222
Course title: Advanced Machine Learning with Graphs
Instructor: Dr. Yangqiu Song
Room: 3518
Telephone: 2358-6987
Email:
WWW Page: https://cse.hkust.edu.hk/~yqsong/

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

Course description:
This course will introduce a number of advanced learning methods and modeling principles for analyzing large-scale complex data structures and graph data. Topics covered include semi-supervised learning, spectral graph theory, deep graph modeling, knowledge graph modeling, etc., as well as open research problems in this area.

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):
Topics - Briefly outline
* Introduction - Introduction to the course and context of the content.
* Graph based semi-supervised learning - Spectral graph theory, graph Laplacian
* Network embedding - Deepwalk, node2vec, heterogeneous information network embeddings, etc.
* Graph neural networks - General graph neural networks: Graph CNN, GraphSage, Message Passing Networks
* Graph isomorphism and subgraph isomorphisms - Graph isomorphism networks and applications such as summary statistics, counting, other NP hard problems
* Knowledge Graphs - Knowledge graph embeddings, Neural graph databases

Grading scheme: Letter grades from F to A+
Assignment: 10%
Project: 30%
Presentation: 10%
Final exam: 40%

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

Minimum CGA required for UG students: 3.7

Course code: COMP 5331
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 (4th edition).

Reference books/materials:
Introduction to Data Mining. Pang-Ning Tan, Michael Steinbach, Vipin Kumar Boston. 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 5621
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: COMP 4621

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: 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 in algorithmic techniques.

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):
* Fixed-parameter algorithms
* Amortized analysis
* Randomized algorithms
* Hashing
* Tail inequalities and random sampling
* Online algorithms
* Streaming 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: COMP 6311F
Course title: Advanced Data Analytics
Instructor: Prof. Song Guo
Room: CYT-3006A
Telephone: 8833
Email:
WWW Page: https://cse.hkust.edu.hk/~songguo/

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

Course description:
The "Advanced Data Analytics" course is designed to provide aspiring PhD students with a comprehensive understanding of cutting-edge techniques in the field of data analysis. Through a blend of theoretical concepts and hands-on practical exercises, students will delve into various domains of data mining and analytics. The course begins with an exploration of classical data mining approaches, covering topics such as graph analysis, text mining, image processing, and regression analysis. Students will then learn the fundamentals of distributed processing using industry-leading platforms like MapReduce, Hadoop, and Spark. The curriculum further delves into the exciting realm of deep learning. Students will also engage with the intriguing domains of federated learning and edge intelligence. The course will address the critical aspect of trustworthy AI, equipping students with knowledge about both potential attacks and defense mechanisms.

Background: Machine learning basics, Mathematics: multivariable calculus, linear algebra and matrix analysis, probability and statistics, Python

Course objective:
Data analytics is a fundamental research field and is crucial for the AI models deploying in real- world applications.

Course outline/content (by major topics):

  1. Introduction to Advanced Data Analytics: Concepts, Challenges, and Applications
  2. Theoretical Foundations of Data Analytics: Statistical Inference, Hypothesis Testing, and Experimental Design
  3. Advanced Classical Data Mining: Graph Mining, Text Mining, Image Mining, and Regression Analysis
  4. Scalable Distributed Processing for Big Data Analytics: MapReduce Paradigm, Apache Spark, Kubernetes Container Orchestration
  5. Deep Learning (DL) for Complex Data Analytics: Modern Neural Networks, Computer Vision, Recommendation Systems
  6. Federated Learning (FL) and Edge Intelligence: Collaborative and Privacy-Preserving Data Analytics in Distributed Environments; Trustworthy AI and Security

Textbooks:
NIL

Reference books/materials:
Data Science and Machine Learning: Making Data-Driven Decisions

Grading scheme:
* Paper Presentations: (25%)
* Paper Summaries (15%)
* Class Participation: (15%)
* Term Project: (45%)

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: 3.7

Please visit Class Schedule & Quota (Fall 2023) for the timetable and quota.

Archive of past courses

Last modified on 2023-08-16.