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 (6417)	Mo 04:30PM - 05:20PM	Rm 101, W1	BAI, Ge CHEN, Huangxun CHU, Xiaowen KAN, Ge Lin LIANG, Junwei QIN, Chengwei RIKOS, APOSTOLOS WANG, Xin WANG, Zeyu XIE, Sihong XIE, Zeke YANG, Menglin YUE, Yutao	100	55	45	0	The class will be delivered by the following instructors and contents as below: W1: Zeke XIE - Overview of Deep Learning Theory W2: Yutao YUE - Towards Conscious AI W3: Menglin YANG - Foundation of LLM: From Pretraining, Mid Training to Post Training W4: Xiaowen CHU - Magic of Low-Precision Computing behind LLMs W5: Sihong XIE - Interpretable AI W6: Xin WANG - Foundations of Quantum Computing W7: Ge BAI - Quantum Entanglement and Applications W8: Chengwei Qin - LLM Reasoning and Agent W9: Zeyu WANG - Toward Synergistic Human-AI Content Creation W10: Huangxun CHEN - Overview of Embedded AI W11: Ge Lin Kan - Generative AI for Urban Digital Twin W12: Junwei Liang - Towards General Service Embodied AI W13: Apostolos RIKOS - AI and Multi-Agent Networks

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).

AIAA 2205 - Introduction to Artificial Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6400)	Mo 09:00AM - 11:50AM	Rm 148, E1	LIU, Li	50 Quota/Enrol/Avail For UG year 2 & above students: 50/36/14	36	14	0
L02 (6401)	We 01:30PM - 04:20PM	Rm 147, E1	CHEN, Jintai	50 Quota/Enrol/Avail For UG year 2 & above students: 50/32/18	32	18	0

COURSE INFO

PRE-REQUISITE	UFUG 1103 OR UFUG 1106
DESCRIPTION	This course aims to teach students the basic math concepts for Artificial Intelligence (AI). Key topics include fundamental Linear Algebra (Matrix Calculations, Norms, Eigenvectors and Eigenvalues), Calculus (Derivative, Taylor series, Multivariate Calculus), and Probability Theory (Distributions, Statistics of Random Variables, Bayes’ theorem). With these mathematical concepts, some basic principles of numerical optimization and typical AI algorithms (Gradient Descent, Maximum-likelihood, Regression, Least Square Estimation, Spectral Clustering, Matrix Decomposition, etc.) will also be introduced as examples to better relate math to AI. The approach of this course is specifically AI application oriented, aiming to help students to quickly establish a fundamental mathematical knowledge structure for AI studies. Through this course, students will acquire the fundamental mathematical concepts required for AI, understand the connections between AI and mathematics, and get prepared to learn the mathematical principles, formulas, inductions, and relevant proofs for advanced AI algorithms.

PRE-REQUISITE

UFUG 1103 OR UFUG 1106

DESCRIPTION

This course aims to teach students the basic math concepts for Artificial Intelligence (AI). Key topics include fundamental Linear Algebra (Matrix Calculations, Norms, Eigenvectors and Eigenvalues), Calculus (Derivative, Taylor series, Multivariate Calculus), and Probability Theory (Distributions, Statistics of Random Variables, Bayes’ theorem). With these mathematical concepts, some basic principles of numerical optimization and typical AI algorithms (Gradient Descent, Maximum-likelihood, Regression, Least Square Estimation, Spectral Clustering, Matrix Decomposition, etc.) will also be introduced as examples to better relate math to AI. The approach of this course is specifically AI application oriented, aiming to help students to quickly establish a fundamental mathematical knowledge structure for AI studies. Through this course, students will acquire the fundamental mathematical concepts required for AI, understand the connections between AI and mathematics, and get prepared to learn the mathematical principles, formulas, inductions, and relevant proofs for advanced AI algorithms.

AIAA 2711 - Mathematics for AI (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail
L01 (6418)	Mo 01:30PM - 02:50PM	Rm 101, E1	ZHONG, Bingzhuo	90 Quota/Enrol/Avail Year 2 UG students: 88/88/0 Year 3 UG Students: 2/2/0	90	0
L01 (6418)	Fr 09:00AM - 10:20AM	Rm 101, E1	ZHONG, Bingzhuo		90	0
L02 (6419)	Mo 09:00AM - 11:50AM	Lecture Hall C	RIKOS, APOSTOLOS	90 Quota/Enrol/Avail Year 2 UG students: 85/58/27 Year 3 UG Students: 5/1/4	59	31

COURSE INFO

PRE-REQUISITE	UFUG 2104 AND (UFUG 2601 OR UFUG 2602)
DESCRIPTION	The implementation of autonomous systems requires agents to learn how to make decisions. Reinforcement learning is a powerful paradigm for achieving such a goal, and it is relevant to an enormous range of tasks, including robotics, game playing, operations research, healthcare and more. This course provides a solid introduction to the field of reinforcement learning. Students learn about the core challenges and approaches, including generalization and exploration. Through the combination of lectures, written and coding assignments, and course projects, students are equipped with modeling and learning algorithm techniques for sequential decision-making problems. Assignments include the basics of reinforcement learning as well as deep reinforcement learning — an extremely promising new area that combines deep learning advancements with reinforcement learning.

PRE-REQUISITE

UFUG 2104 AND (UFUG 2601 OR UFUG 2602)

DESCRIPTION

The implementation of autonomous systems requires agents to learn how to make decisions. Reinforcement learning is a powerful paradigm for achieving such a goal, and it is relevant to an enormous range of tasks, including robotics, game playing, operations research, healthcare and more. This course provides a solid introduction to the field of reinforcement learning. Students learn about the core challenges and approaches, including generalization and exploration. Through the combination of lectures, written and coding assignments, and course projects, students are equipped with modeling and learning algorithm techniques for sequential decision-making problems. Assignments include the basics of reinforcement learning as well as deep reinforcement learning — an extremely promising new area that combines deep learning advancements with reinforcement learning.

AIAA 3053 - Reinforcement Learning: Principles and Methods (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6420)	Fr 12:00PM - 02:50PM	Rm 101, E1	RIKOS, APOSTOLOS	80	76	4	0

[Matching between Lecture & Lab required]

COURSE INFO

PRE-REQUISITE	Prerequisites: UFUG 2601 OR UFUG 2602
DESCRIPTION	This course aims to provide a solid understanding of modern Computer Vision. It starts with essential backgrounds in image processing and classical vision methods, then transitions to contemporary learning-based techniques. Students will master core architectures including CNNs, Transformers, and generative models like GANs and Diffusion. Advanced modules explore detection, segmentation, and learning-based 3D vision. The course emphasizes problem-solving through a practical mini-project, encouraging students to apply these algorithms to real-world needs such as biomedical analysis or AR/VR. Students will finish the course ready to conduct independent research and develop innovative vision solutions.

PRE-REQUISITE

Prerequisites: UFUG 2601 OR UFUG 2602

DESCRIPTION

This course aims to provide a solid understanding of modern Computer Vision. It starts with essential backgrounds in image processing and classical vision methods, then transitions to contemporary learning-based techniques. Students will master core architectures including CNNs, Transformers, and generative models like GANs and Diffusion. Advanced modules explore detection, segmentation, and learning-based 3D vision. The course emphasizes problem-solving through a practical mini-project, encouraging students to apply these algorithms to real-world needs such as biomedical analysis or AR/VR. Students will finish the course ready to conduct independent research and develop innovative vision solutions.

AIAA 3201 - Introduction to Computer Vision (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol
L01 (6424)	Tu 01:30PM - 03:20PM	Rm 147, E1	CHEN, Yingcong	48	48
L02 (6425)	Th 01:30PM - 03:20PM	Rm 134, E1	WANG, Hao	48	48
LA01 (6426)	Tu 03:30PM - 04:20PM	Rm 228, E1	CHEN, Yingcong	48	48
LA02 (6427)	Th 03:30PM - 04:20PM	Rm 134, E1	WANG, Hao	48	48

COURSE INFO

PRE-REQUISITE	UFUG 2601 OR UFUG 2602
EXCLUSION	DSAA 3051
CROSS CAMPUS COURSE EQUIVALENCE	COMP 4221
DESCRIPTION	This course provides a comprehensive journey into Natural Language Processing, first establishing the essential computational fundamentals of words, syntax, and semantics, and then advancing to the modern architectures that define the LLM era, such as transformers and GPT. You will learn to bridge classic techniques with cutting-edge applications—mastering pre-training, fine-tuning, and prompt engineering—while critically examining the advanced capabilities and profound responsibilities surrounding fairness, security, and interpretability. Through a blend of foundational theory and hands-on projects, this course equips you to build, innovate, and lead in the rapidly evolving field of applied NLP.

AIAA 4051 - Introduction to Natural Language Processing (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6428)	MoWe 12:00PM - 01:20PM	Rm 101, E1	XIE, Sihong	80	80	0	0

COURSE INFO

PRE-REQUISITE	UFUG 2104 OR AIAA 2711
CROSS CAMPUS COURSE EQUIVALENCE	COMP 4641
DESCRIPTION	This course is an introduction to social information network analysis and engineering. Students will learn both mathematical and programming knowledge for analyzing the structures and dynamics of typical social information networks (e.g., Facebook, Twitter, and MSN). They will also learn how social metrics can be used to improve computer system design as people are the networks. It will cover topics such as small world phenomenon; contagion, tipping and influence in networks; models of network formation and evolution; the web graph and PageRank; social graphs and community detection; measuring centrality; greedy routing and navigations in networks; introduction to game theory and strategic behavior; social engineering; and principles of computer system design.

AIAA 4641 - Social Information Network Analysis and Engineering (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail
L01 (6429)	Mo 06:00PM - 07:20PM	Rm 122, E1	DAI, Enyan	65	55	10
LA01 (6430)	Tu 12:00PM - 01:20PM	Rm 227, E1	DAI, Enyan	65	55	10
LA01 (6430)	Tu 12:00PM - 01:20PM	Rm 228, E1	DAI, Enyan	65	55	10

COURSE INFO

PRE-REQUISITE	DSAA 2043
DESCRIPTION	The course aims to introduce advanced methods for designing and analyzing algorithms for solving tough problems. The topics cover useful advanced data structures, graph algorithms, heuristic searching algorithms and optimization algorithms that promote the development of AI. Also, students will learn algorithm design and analysis skills for computationally intractable problems, such as NP-completeness, randomized algorithms, approximation algorithms and amortized analysis. Additional topics, such as string matching, geometric and number-theoretic algorithms, will also be introduced. The course presents the topics at an introductory level and aims at senior undergraduate students. Through the class, students will develop a deeper understanding of algorithm design and demonstrate a basic understanding of the principles of advanced algorithms.

PRE-REQUISITE

DSAA 2043

DESCRIPTION

The course aims to introduce advanced methods for designing and analyzing algorithms for solving tough problems. The topics cover useful advanced data structures, graph algorithms, heuristic searching algorithms and optimization algorithms that promote the development of AI. Also, students will learn algorithm design and analysis skills for computationally intractable problems, such as NP-completeness, randomized algorithms, approximation algorithms and amortized analysis. Additional topics, such as string matching, geometric and number-theoretic algorithms, will also be introduced. The course presents the topics at an introductory level and aims at senior undergraduate students. Through the class, students will develop a deeper understanding of algorithm design and demonstrate a basic understanding of the principles of advanced algorithms.

AIAA 4711 - Introduction to Advanced Algorithmic Techniques (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6431)	Fr 09:00AM - 11:50AM	Rm 201, W2	BAI, Ge	40	17	23	0

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course covers popular topics in computer vision, which includes high-level tasks like image classification, object detection, image segmentation, and low-level tasks like image generation, image enhancement, image-to-image translation, etc.

AIAA 5026 - Computer Vision and Its Applications (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6033)	Th 01:30PM - 04:20PM	Lecture Hall C	CHEN, Yingcong	130	130	0	0

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 (6034)	Mo 01:30PM - 04:20PM	Rm 101, W1	LIANG, Junwei	120	85	35	0

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course introduces potential security and privacy vulnerabilities in Artificial Intelligence (AI) and covers basic and advanced protections. Topics include security and privacy risks in AI technologies, the goal of C.I.A. (Confidentiality, Integrity and Availability) in AI technologies, basic and advanced cryptography, protocol designs for AI security and privacy, etc.

AIAA 5033 - AI Security and Privacy (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6035)	Mo 09:00AM - 11:50AM	Rm 102, W4	ZHONG, Bingzhuo	80	72	8	0

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 (6036)	Fr 09:00AM - 11:50AM	Rm 150, E1	XIE, Zeke	40	20	20	0

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course focuses on the Artificial Intelligence (AI) techniques and applications in multimodal tasks, which involve processing, fusing, and generating contents from multiple data modalities, such as images, videos, text etc. The course will cover the challenges, state-of-the-art methods, as well as hands-on experience in implementing and evaluating multi-modal deep learning models.

AIAA 5048 - Multimodal Artificial Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6038)	Tu 01:30PM - 04:20PM	Rm 101, W4	WANG, Hao	45	44	1	0

COURSE INFO

VECTOR

[3-0-0:3]

DESCRIPTION

The study of consciousness is referred to as the "ultimate challenge of artificial intelligence." This course provides instruction and discussions in the field of machine consciousness. The main content includes an introduction to consciousness research, mainstream theories of consciousness, research on self-awareness, attention mechanisms, optimization of intelligent agent goals, subjectivity and affective computing, consciousness modeling and evaluation of artificial intelligence systems, and analysis and control of risks related to machine consciousness. Through this course, participants can gain a fairly comprehensive and in-depth understanding of the research history and current status of the field of machine consciousness, and engage in collaborative research on several specific issues.

AIAA 5050 - Machine Consciousness (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6040)	Fr 03:00PM - 05:50PM	Rm 228, E2	YUE, Yutao	50	44	6	0

VECTOR	3 credits
DESCRIPTION	An independent research project carried out under the supervision of a faculty member. Graded P or F.

AIAA 6091A - Independent Study (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6770)	TBA	TBA	TBA	10	0	10	0	Instructor Consent Required

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 (6771)	TBA	TBA	TBA	10	5	5	0	Instructor Consent Required

COURSE INFO

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

AIAA 6101 - Artificial Intelligence Seminar I (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
T01 (6366)	We 01:30PM - 02:20PM	Lecture Hall C	ZHONG, Bingzhuo	180	180	0	0

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 (6003)	TBA	No room required	TBA	999	93	906	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 (6004)	TBA	No room required	TBA	999	56	943	0