AIAA - HKUST Class Schedule & Quota

COURSE INFO

DESCRIPTION	This course provides guidance to undergraduate students of the AI major for their academic path and future. This course is mostly introductory and aims to inspire UG students for their academic path development and growth of maturity during their UG study. Activities may include seminars, workshops, advising and sharing sessions, interaction with faculty and teaching staff, and discussion with student peers or alumni. Graded P or F.

DESCRIPTION

This course provides guidance to undergraduate students of the AI major for their academic path and future. This course is mostly introductory and aims to inspire UG students for their academic path development and growth of maturity during their UG study. Activities may include seminars, workshops, advising and sharing sessions, interaction with faculty and teaching staff, and discussion with student peers or alumni. Graded P or F.

AIAA 1010 - Academic Orientation for AI Students (1 unit)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6363)	01-SEP-2025 - 05-SEP-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	DAI, Enyan	155	155	0	1	The class will be delivered by the following instructors as below. W1-Enyan Dai W2-Bingzhuo Zhong W3-Xin Wang W4-Sihong Xie W5-Menglin Yang W6-Yingcong Chen W7-Junwei Liang W8-Changhao Chen W9-Zeke Xie W10-Yutao Yue W11-Li LIU W12-Xuming Hu W13-Apostolos Rikos
	08-SEP-2025 - 12-SEP-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	ZHONG, Bingzhuo
	15-SEP-2025 - 19-SEP-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	WANG, Xin
	22-SEP-2025 - 26-SEP-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	XIE, Sihong
	29-SEP-2025 - 11-OCT-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	YANG, Menglin
	13-OCT-2025 - 17-OCT-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	CHEN, Yingcong
	20-OCT-2025 - 24-OCT-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	LIANG, Junwei
	27-OCT-2025 - 31-OCT-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	CHEN, Changhao
	03-NOV-2025 - 07-NOV-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	XIE, Zeke
	10-NOV-2025 - 14-NOV-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	YUE, Yutao
	17-NOV-2025 - 21-NOV-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	LIU, Li
	24-NOV-2025 - 28-NOV-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	HU, Xuming
	01-DEC-2025 - 05-DEC-2025 Tu 09:00AM - 09:50AM	Lecture Hall B	RIKOS, APOSTOLOS

COURSE INFO

PRE-REQUISITE	UFUG 2601 OR UFUG 2602
DESCRIPTION	The objective of this course is to present an overview of the principles and practices of AI and to address complex real-world problems. Through introduction of AI tools and techniques, the course helps students develop a basic understanding of problem solving, search, theorem proving, knowledge representation, reasoning and planning methods of AI; and develop practical applications in vision, language, and so on. Topics include foundations (search, knowledge representation, machine learning and natural language understanding) and applications (data mining, decision support systems, adaptive web sites, web log analysis).

PRE-REQUISITE

UFUG 2601 OR UFUG 2602

DESCRIPTION

The objective of this course is to present an overview of the principles and practices of AI and to address complex real-world problems. Through introduction of AI tools and techniques, the course helps students develop a basic understanding of problem solving, search, theorem proving, knowledge representation, reasoning and planning methods of AI; and develop practical applications in vision, language, and so on. Topics include foundations (search, knowledge representation, machine learning and natural language understanding) and applications (data mining, decision support systems, adaptive web sites, web log analysis).

AIAA 2205 - Introduction to Artificial Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6364)	TuTh 01:30PM - 02:50PM	Rm 102, E4	LIU, Li	100	90	10	1
L02 (6365)	TuTh 04:30PM - 05:50PM	Rm 101, E1	DAI, Enyan	93	64	29	0

COURSE INFO

DESCRIPTION	This introductory course surveys the explosive area of AI ethics and illuminates relevant AI concepts with no prior background needed. Key topics include Fake News Bots; AI Driven Social Media Displacing Traditional Journalism; drone Warfare; Elimination of Traditional Jobs; Privacy-violating Advertising; Monopolistic Network Effects; Biased AI Decision/Recognition Algorithms; Deepfakes; Autonomous Vehicles; Automated Hedge Fund Trading, etc. Through the course, students will be able to understand how human civilization will survive amid the rise of AI, what are the new rules in the new era, how to preserve ethics when facing the threats of extinction and what are engineers’ and entrepreneurs’ ethical responsibilities.

DESCRIPTION

This introductory course surveys the explosive area of AI ethics and illuminates relevant AI concepts with no prior background needed. Key topics include Fake News Bots; AI Driven Social Media Displacing Traditional Journalism; drone Warfare; Elimination of Traditional Jobs; Privacy-violating Advertising; Monopolistic Network Effects; Biased AI Decision/Recognition Algorithms; Deepfakes; Autonomous Vehicles; Automated Hedge Fund Trading, etc. Through the course, students will be able to understand how human civilization will survive amid the rise of AI, what are the new rules in the new era, how to preserve ethics when facing the threats of extinction and what are engineers’ and entrepreneurs’ ethical responsibilities.

AIAA 2290 - Ethics, Privacy and Security in Artificial Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6366)	Fr 09:00AM - 11:50AM	Rm 149, E1	HU, Xuming	50	50	0	15
L02 (6367)	Fr 01:30PM - 04:20PM	Rm 149, E1	HU, Xuming	50	50	0	6

COURSE INFO

PRE-REQUISITE	UFUG 2601 OR UFUG 2602
DESCRIPTION	This course aims to teach the students to program with Python and use Python to develop fundamental Artificial Intelligence (AI) applications. AI-oriented as well as generic programming concepts and skills will be taught in Python language. Key topics include fundamental Python features, principles, and syntax; programming in Python for numerical computation with efficient arrays and matrix classes; programming in Python for scientific analysis with widely adopted scientific libraries; fundamental development of Python web crawler for data collection; data processing and analysis with Python; machine learning model building and evaluation in Python; fundamental usage of deep learning frameworks. Students will practice programming skills for AI and get familiar with the overall workflow on building AI systems as a team through the course project. Through the course, students will be able to understand programming principles for AI research and development and master the skills to build simple AI applications to solve practical problems.

PRE-REQUISITE

UFUG 2601 OR UFUG 2602

DESCRIPTION

This course aims to teach the students to program with Python and use Python to develop fundamental Artificial Intelligence (AI) applications. AI-oriented as well as generic programming concepts and skills will be taught in Python language. Key topics include fundamental Python features, principles, and syntax; programming in Python for numerical computation with efficient arrays and matrix classes; programming in Python for scientific analysis with widely adopted scientific libraries; fundamental development of Python web crawler for data collection; data processing and analysis with Python; machine learning model building and evaluation in Python; fundamental usage of deep learning frameworks. Students will practice programming skills for AI and get familiar with the overall workflow on building AI systems as a team through the course project. Through the course, students will be able to understand programming principles for AI research and development and master the skills to build simple AI applications to solve practical problems.

AIAA 3102 - Python Programming for Artificial Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6368)	Th 09:00AM - 11:50AM	Rm 148, E1	QIN, Chengwei	50 Quota/Enrol/Avail BEng (AI) Year 3 Students: 50/19/31	19	31	22
L02 (6369)	Tu 09:00AM - 11:50AM	Rm 148, E1	SHU, Yao	50 Quota/Enrol/Avail BEng (AI) Year 3 Students: 50/48/2	48	2	4

COURSE INFO

PRE-REQUISITE	UFUG 2601 OR UFUG 2602 OR DSAA 1001
DESCRIPTION	This course explains the fundamental principles, uses, and some technical details of data mining techniques through lectures and real-world case studies. The emphasis is on understanding the business applications of data mining techniques. The mechanics of how data analytics techniques work will also be discussed as it is essential to the understanding of the fundamental concepts and business applications.

PRE-REQUISITE

UFUG 2601 OR UFUG 2602 OR DSAA 1001

DESCRIPTION

This course explains the fundamental principles, uses, and some technical details of data mining techniques through lectures and real-world case studies. The emphasis is on understanding the business applications of data mining techniques. The mechanics of how data analytics techniques work will also be discussed as it is essential to the understanding of the fundamental concepts and business applications.

AIAA 3111 - Introduction to Data Mining (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6370)	Mo 06:00PM - 08:50PM	Rm 102, W1	CHEN, Jintai	50 Quota/Enrol/Avail BEng (AI) Year 3 Students: 50/23/27	23	27	6
L02 (6371)	We 06:00PM - 08:50PM	Rm 102, W1	LIU, Hao	50 Quota/Enrol/Avail BEng (AI) Year 3 Students: 50/32/18	32	18	7

COURSE INFO

PRE-REQUISITE	DSAA 2011
DESCRIPTION	Learning and optimization serve as the foundational block for many artificial intelligence algorithms. Our initial focus is on convex analysis and on modeling problems as convex problems, while later on in the course we will shift the focus to different algorithms for convex optimization and nonconvex optimization. The techniques introduced in this course will be motivated by needs of problems and applications in Machine Learning and Deep Learning. The topics range from foundational material to cutting-edge trends.

PRE-REQUISITE

DSAA 2011

DESCRIPTION

Learning and optimization serve as the foundational block for many artificial intelligence algorithms. Our initial focus is on convex analysis and on modeling problems as convex problems, while later on in the course we will shift the focus to different algorithms for convex optimization and nonconvex optimization. The techniques introduced in this course will be motivated by needs of problems and applications in Machine Learning and Deep Learning. The topics range from foundational material to cutting-edge trends.

AIAA 3225 - Learning and Optimization for Artificial Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6399)	Th 03:00PM - 05:50PM	Rm 122, E1	GONG, Zijun WANG, Xin	50 Quota/Enrol/Avail BEng (AI) Year 3 Students: 50/21/29	22	28	0
L02 (6400)	Tu 06:00PM - 08:50PM	Rm 102, W4	GONG, Zijun WANG, Xin	50 Quota/Enrol/Avail BEng (AI) Year 3 Students: 50/19/31	19	31	0

COURSE INFO

PRE-REQUISITE	UFUG1103 AND UFUG2104
DESCRIPTION	This course introduces the fundamentals of embodied AI. Students will explore key principles and algorithms to build modern autonomous embodied AI systems. Key topics include machine perception, planning and decision-making algorithms. Through this course, students will learn and practice the foundational principles, techniques, and tools to build new embodied autonomous AI systems.

AIAA 4220 - Introduction to Embodied AI (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6679)	Mo 01:30PM - 04:20PM	Rm 149, E1	LIANG, Junwei	50 Quota/Enrol/Avail BEng (AI) Year 3 Students: 50/28/22	30	20	1
L02 (6680)	We 01:30PM - 04:20PM	Rm 149, E1	CHEN, Changhao	50 Quota/Enrol/Avail BEng (AI) Year 3 Students: 50/5/45	7	43	0

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course will introduce the fundamental principles, uses, and technical details of data mining techniques by lectures and real-world case studies. The emphasis is on understanding the basic data mining techniques and their applications. We will discuss the mechanics of how data analytics techniques work as is necessary to understand the fundamental concepts and real-world applications.

AIAA 5030 - Foundations of Data Mining (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6001)	We 09:00AM - 11:50AM	Rm 102, E4	YANG, Menglin	60 Quota/Enrol/Avail PhD (AI): 20/17/3	57	3	10

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course covers how to program with Python and use it to solve practical problems in Artificial Intelligence. Topics include basic Python usage (e.g., syntax, data structure, etc.) and important packages for data analysis and machine learning applications (e.g., NumPy, SciPy, etc.). The students will be guided to practice on simple artificial intelligence tasks.

AIAA 5031 - Introduction to Computing Using Python (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6002)	Tu 09:00AM - 11:50AM	Rm 102, E4	CHEN, Huangxun	80 Quota/Enrol/Avail PhD (AI): 30/30/0	79	1	21

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course aims to provide students with an overview of Artificial Intelligence (AI) principles and techniques. Key topics include machine learning, search, game theories, Markov decision process, constraint satisfaction problems, Bayesian networks, etc. Through this course, students will learn and practice the foundational principles, techniques and tools to tackle new AI problems.

AIAA 5032 - Foundations of Artificial Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6003)	Th 09:00AM - 11:50AM	Rm 101, E1	LIU, Li	80	80	0	38

COURSE INFO

VECTOR

[3-0-0:3]

DESCRIPTION

Artificial Intelligence technologies have been maturing and are deployed in real-world applications, such as healthcare, entertainment, business, scientific research, military, etc. In all these domains, the decisions made by AI algorithms can critically impact individuals, organizations and society. The designers, auditors, and users of AI technologies thus need to be equipped with the capabilities to understand, analyze, and eventually discipline these algorithms in the broader contexts. This course will introduce students to the latest research of responsible AI and explore these capabilities in both theoretical and practical ways. Topics include but are not limited to theories and algorithms of secure machine learning, fair machine learning, interpretable AI, and case studies involving natural language processing, computer vision, and reinforcement learning.

AIAA 5047 - Responsible Artificial Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6004)	Fr 09:00AM - 11:50AM	Rm 201, W2	XIE, Sihong	50	50	0	24

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course provides a comprehensive introduction to the field of quantum computing. Students will explore fundamental concepts such as quantum bits, quantum circuits, and quantum algorithms. Advanced topics including quantum error correction, quantum information processing, and quantum machine learning will also be covered.

AIAA 5072 - Quantum Computing (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6005)	Mo 01:30PM - 04:20PM	Rm 223, W1	BAI, Ge WANG, Xin	30 Quota/Enrol/Avail PhD (AI): 15/15/0	30	0	8

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course offers an in-depth exploration of Natural Language Processing (NLP), emphasizing transformative neural network architectures like RNNs and transformers. Students will engage with core NLP tasks such as language modeling and machine translation, and examine the impacts of recent innovations like Large Language Models.

AIAA 5088 - Natural Language Processing and Its Applications (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6006)	Th 01:30PM - 04:20PM	Rm 202, W4	HU, Xuming	40 Quota/Enrol/Avail PhD (AI): 30/30/0	40	0	67

VECTOR	[1credit]
DESCRIPTION	An independent research project carried out under the supervision of a faculty member. Graded P or F.

AIAA 6091B - Independent Study (1 unit)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6688)	TBA	TBA	TBA	20	2	18	0	Instructor Consent Required

COURSE INFO

VECTOR	[0-1-0:1]
DESCRIPTION	Series of seminars presenting research problems currently under investigation, presented by faculty, students, and visiting speakers. Students are expected to attend regularly. Continuation of AIAA 6101. Graded P or F.

AIAA 6102 - Artificial Intelligence Seminar II (1 unit)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
T01 (6007)	Fr 01:30PM - 02:20PM	Rm 102, E4	DAI, Enyan	155	144	11	2

COURSE INFO

DESCRIPTION	Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

AIAA 6990 - MPhil Thesis Research (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6008)	TBA	No room required	TBA	999	84	915	0

COURSE INFO

DESCRIPTION	Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

AIAA 7990 - Doctoral Thesis Research (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6010)	TBA	No room required	TBA	999	27	972	0