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 (6249)	Fr 09:00AM - 09:50AM	Rm 102, E4	CHEN, Jintai CHEN, Yingcong DAI, Enyan HU, Xuming LIANG, Junwei LIU, Li RIKOS, APOSTOLOS SUN, Ying WANG, Hao WANG, Xin XIE, Sihong XIE, Zeke YUE, Yutao	100	70	30	0	The class will be delivered by the following instructors as below. Week 1: Junwei LIANG Week 2: Xin WANG Week 3: HAO WANG Week 4: Sihong XIE Week 5: Li LIU Week 6: Zeke XIE Week 7: Yutao YUE Week 8: Xuming HU Week 9: Jintai CHEN Week 10: Yingcong CHEN Week 11: Ying SUN Week 12: Enyan Dai Week 13: Apostolos Rikos

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 (6245)	TuFr 01:30PM - 02:50PM	Rm 102, E4	LIU, Li YUE, Yutao	100	44	56	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 (6250)	MoWe 07:30PM - 08:50PM	Rm 228, E2	HU, Xuming	45	45	0	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	Wait	Remarks
L01 (6247)	MoWe 06:00PM - 07:20PM	Rm 102, E4	ZHONG, Bingzhuo	100	93	7	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 (6001)	Th 09:00AM - 11:50AM	Rm 202, E3	LIANG, Junwei	40	38	2	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 (6002)	Fr 09:00AM - 11:50AM	Rm 228, E2	ZHONG, Bingzhuo	80	55	25	0

COURSE INFO

VECTOR

[3-0-0:3]

DESCRIPTION

Learning to make good decisions is one of the keys to autonomous systems. This course will focus on Reinforcement Learning (RL), a currently very active subfield of artificial intelligence, and it will discuss selectively a number of algorithmic topics including Markov Decision Process, Q-Learning, function approximation, exploration and exploitation, policy search, imitation learning, model-based RL and optimal control. This course provides both the foundations and techniques for developing RL and deep RL algorithms that interact with physical environments, and real application cases of RL will be introduced. Basic knowledge of machine learning and mathematical optimization are expected for this course.

AIAA 5034 - Reinforcement Learning and Optimization (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6005)	Fr 01:30PM - 04:20PM	Rm 101, W1	RIKOS, APOSTOLOS	60	38	22	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 (6004)	Tu 01:30PM - 04:20PM	Rm 102, W1	WANG, Hao	30	30	0	0

COURSE INFO

VECTOR

[3-0-0:3]

DESCRIPTION

Graph mining methods have been investigated for various applications including financial analysis, traffica prediction, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that existing graph mining methods can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data, and lack interpretability. In this course, representative graph mining models and their inner mechanisms will be discussed. Then, we will introduce the trustworthy graph mining methods in privacy, robustness, fairness, and explainability.

AIAA 6011A - Trustworthy Graph Mining (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6010)	TuTh 10:30AM - 11:50AM	Rm 233, W1	DAI, Enyan	30	6	24	0

COURSE INFO

VECTOR

[3-0-0:3]

DESCRIPTION

This course delves into essential machine learning and deep learning algorithms for healthcare applications, covering areas such as life sciences discovery, medical image analysis, biosignal processing, medical data mining, and foundational models for electronic health records. Core topics include key methodologies for algorithm design, commonly used data structures, and widely adopted algorithms tailored to diverse healthcare challenges. Additionally, the course highlights the latest advancements in this rapidly evolving field.

AIAA 6011B - Frontier of "AI for Healthcare" (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6011)	Mo 01:30PM - 04:20PM	Rm 102, E1	CHEN, Jintai	30	16	14	0

VECTOR	[1-3 credit(s)]
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 (6515)	TBA	TBA	TBA	20	7	13	0	Instructor Consent Required

VECTOR	[1-3 credit(s)]
DESCRIPTION	An independent research project carried out under the supervision of a faculty member. Graded P or F.

AIAA 6091B - Independent Study (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6541)	TBA	TBA	TBA	20	5	15	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 (6006)	We 01:30PM - 02:20PM	Rm 101, W1	CHEN, Jintai	155	141	14	0	The class is delivered in mixed mode.

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 (6007)	TBA	No room required	TBA	999	50	949	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 (6008)	TBA	No room required	TBA	999	25	974	0