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 (6541)	Fr 12:00PM - 12:50PM	Lecture Hall B	BAI, Ge CHEN, Changhao CHEN, Yingcong CHU, Xiaowen DAI, Enyan HU, Zhiming LIU, Li RIKOS, APOSTOLOS SHU, Yao XIE, Zeke YANG, Menglin YUE, Yutao ZHONG, Bingzhuo	200	0	200	0

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	Avail
L01 (6550)	Mo 10:30AM - 11:50AM	Rm 102, W4	CHEN, Jintai	96 Quota/Enrol/Avail For UG year 2 & above students: 96/0/96	96
L01 (6550)	We 10:30AM - 11:50AM	Rm 101, W1	CHEN, Jintai		96
L02 (6931)	MoWe 10:30AM - 11:50AM	Lecture Hall A	TBA	100 Quota/Enrol/Avail For UG year 2 & above students: 100/0/100	100

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	Avail
L01 (6551)	Mo 04:30PM - 05:50PM	Rm 101, E1	HU, Xuming LONG, Yan	80 Quota/Enrol/Avail For UG year 2 & above students: 80/0/80	80
L01 (6551)	Fr 12:00PM - 01:20PM	Rm 101, E1	HU, Xuming LONG, Yan		80
L02 (6552)	WeFr 01:30PM - 02:50PM	Lecture Hall A	HU, Xuming LONG, Yan	80 Quota/Enrol/Avail For UG year 2 & above students: 80/0/80	80

COURSE INFO

PRE-REQUISITE	(UFUG 2102 OR UFUG 2103) AND AIAA 2711
DESCRIPTION	Quantum computing represents a revolutionary computing paradigm with transformative potential for next-generation AI. This course provides a comprehensive introduction to quantum computing and quantum AI, guiding students from foundational concepts—such as qubits and quantum circuits—to advanced topics, including quantum algorithms, quantum error correction, quantum simulation, and quantum programming. The curriculum also introduces the foundations of the emerging area of quantum AI, covering quantum neural networks, quantum machine learning methods, and AI applications in quantum computing. It spans material from basic principles to the latest cutting-edge developments.

PRE-REQUISITE

(UFUG 2102 OR UFUG 2103) AND AIAA 2711

DESCRIPTION

Quantum computing represents a revolutionary computing paradigm with transformative potential for next-generation AI. This course provides a comprehensive introduction to quantum computing and quantum AI, guiding students from foundational concepts—such as qubits and quantum circuits—to advanced topics, including quantum algorithms, quantum error correction, quantum simulation, and quantum programming. The curriculum also introduces the foundations of the emerging area of quantum AI, covering quantum neural networks, quantum machine learning methods, and AI applications in quantum computing. It spans material from basic principles to the latest cutting-edge developments.

AIAA 3072 - Foundations of Quantum Computing and Quantum AI (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6553)	MoWe 12:00PM - 01:20PM	Rm 101, E4	BAI, Ge WANG, Xin	40 Quota/Enrol/Avail UG Year 3&4 AI students: 40/0/40	0	40	0

COURSE INFO

PRE-REQUISITE

UFUG 2601 OR UFUG 2602

DESCRIPTION

This undergraduate course provides a solid foundation in Python programming tailored for artificial intelligence applications. It begins with core Python concepts, including data types, control flow, functions, and object-oriented programming. Students then explore machine learning essentials using NumPy, Matplotlib, and scikit-learn for scientific computing, visualization, and implementing basic supervised and unsupervised models. The course further introduces deep learning with PyTorch, covering neural networks, optimization, transformers, large language models (LLMs), and AI agents. A distinctive module focuses on programming with AI, examining AI coding copilots, their workflows, practical strengths and limitations, and how to collaborate effectively with these tools. The course concludes with a final capstone project in which students design, implement, and present a substantial AI application, demonstrating their ability to apply Python both to build AI systems and to leverage AI tools in modern development.

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

Section	Date & Time	Room	Instructor	Quota	Avail
L01 (6554)	Mo 04:30PM - 05:50PM	Lecture Hall A	QIN, Chengwei	100 Quota/Enrol/Avail UG Year 3&4 AI students: 100/0/100	100
L01 (6554)	Fr 12:00PM - 01:20PM	Lecture Hall A	QIN, Chengwei		100
L02 (6555)	We 01:30PM - 02:50PM	Rm 102, W4	TBA	96 Quota/Enrol/Avail UG Year 3&4 AI students: 96/0/96	96
L02 (6555)	Fr 01:30PM - 02:50PM	Rm 102, W4	TBA	96 Quota/Enrol/Avail UG Year 3&4 AI students: 96/0/96	96

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	Avail
L01 (6556)	Mo 03:00PM - 04:20PM	Lecture Hall B	LIU, Hao	100 Quota/Enrol/Avail UG Year 3&4 AI students: 100/0/100	100
L01 (6556)	Fr 10:30AM - 11:50AM	Lecture Hall B	LIU, Hao		100
L02 (6932)	Mo 03:00PM - 04:20PM	Lecture Hall A	LIU, Zhidan	100 Quota/Enrol/Avail UG Year 3&4 AI students: 100/0/100	100
L02 (6932)	Fr 10:30AM - 11:50AM	Lecture Hall A	LIU, Zhidan		100

[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	Avail
L01 (6557)	We 09:00AM - 10:50AM	Rm 102, E4	WANG, Hao	100 Quota/Enrol/Avail UG Year 3&4 AI students: 100/0/100	100
L02 (6936)	We 09:00AM - 10:50AM	Lecture Hall B	ZHAO, Tianxiang	100 Quota/Enrol/Avail UG Year 3&4 AI students: 100/0/100	100
LA01 (6558)	We 11:00AM - 11:50AM	Rm 102, E4	WANG, Hao	100	100
LA02 (6937)	Th 04:30PM - 05:20PM	Lecture Hall B	ZHAO, Tianxiang	100	100

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 (6559)	Mo 01:30PM - 02:50PM	Rm 102, E1	GONG, Zijun WANG, Xin	40 Quota/Enrol/Avail UG Year 3&4 AI students: 40/0/40	0	40	0
L01 (6559)	Fr 09:00AM - 10:20AM	Rm 102, E1	GONG, Zijun WANG, Xin	40 Quota/Enrol/Avail UG Year 3&4 AI students: 40/0/40	0	40	0

COURSE INFO

PRE-REQUISITE	AIAA 2205 AND AIAA 2711
DESCRIPTION	This course focuses on human-centered AI techniques that include three main topics: AI for understanding human, human-AI interaction, and human-AI coexistence. On the first topic, this course will introduce AI methods for human visual attention modeling, human body movement modeling, human emotion modeling, human activity and intention recognition, and human cognitive state detection, etc. On the second topic, this course will introduce interactive machine learning, human-centered AI system design, AI assistant and agent, etc. On the third topic, this course will introduce explainable AI, AI ethics, AI fairness, and AI trust. Students are required to accomplish mini course projects to enhance the knowledge and skills that they learn in this course.

PRE-REQUISITE

AIAA 2205 AND AIAA 2711

DESCRIPTION

This course focuses on human-centered AI techniques that include three main topics: AI for understanding human, human-AI interaction, and human-AI coexistence. On the first topic, this course will introduce AI methods for human visual attention modeling, human body movement modeling, human emotion modeling, human activity and intention recognition, and human cognitive state detection, etc. On the second topic, this course will introduce interactive machine learning, human-centered AI system design, AI assistant and agent, etc. On the third topic, this course will introduce explainable AI, AI ethics, AI fairness, and AI trust. Students are required to accomplish mini course projects to enhance the knowledge and skills that they learn in this course.

AIAA 3800 - Human-Centered Artificial Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6560)	We 03:00PM - 05:50PM	Rm 201, E1	HU, Zhiming	40	0	40	0

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 - Natural Language Processing (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6561)	MoWe 12:00PM - 01:20PM	Rm 101, W1	YANG, Menglin	80 Quota/Enrol/Avail UG Year 3&4 AI students: 80/0/80	0	80	0
L02 (6562)	TuTh 09:00AM - 10:20AM	Rm 101, E1	XIE, Sihong	80 Quota/Enrol/Avail UG Year 3&4 AI students: 80/0/80	0	80	0

COURSE INFO

PRE-REQUISITE	(UFUG1103 OR UFUG1106) 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 - Embodied AI (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6563)	Mo 09:00AM - 11:50AM	Rm 233, W1	LIANG, Junwei	40	0	40	0

COURSE INFO

PRE-REQUISITE	DSAA2011 AND UFUG2104
DESCRIPTION	This course is an advanced course in machine learning. It dives into statistical methods that are very popular in modern machine learning systems. It includes topics like supervised learning, dimensional reduction, support vector machines, boosting, sparse methods and deep learning, etc. In this course, we will delve into statistical methods for machine learning. It covers hot topics in statistical learning, aiming to provide students with state-of-the-art statistical tools to enhance modern engineering and science practice.

PRE-REQUISITE

DSAA2011 AND UFUG2104

DESCRIPTION

This course is an advanced course in machine learning. It dives into statistical methods that are very popular in modern machine learning systems. It includes topics like supervised learning, dimensional reduction, support vector machines, boosting, sparse methods and deep learning, etc. In this course, we will delve into statistical methods for machine learning. It covers hot topics in statistical learning, aiming to provide students with state-of-the-art statistical tools to enhance modern engineering and science practice.

AIAA 4432 - Statistical Machine Learning (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6564)	Th 12:00PM - 02:50PM	Rm 147, E1	SHU, Yao	60	0	60	0

COURSE INFO

DESCRIPTION	This course explores the integration of human-computer interaction (HCI) research with advanced artificial intelligence systems to design next-generation interactive experiences. Students will learn to develop immersive 3D environments (Unity/VR/Metaverse) while embedding AI-driven capabilities—such as LLM for adaptive agent behaviors, multi-agent coordination, and natural language dialogue systems—into immersive VR systems. The curriculum emphasizes a human-centered methodology, including formative-summative evaluation, ethical alignment, and summative study to ensure the design could satisfy user needs. Meanwhile, the course will also allow students to learn techniques such as motion capture, and AIGC-assisted content, AI agent during system implementation. Students will gain hands-on practice in architecting intelligent systems that balance technical implementation with user-centric design.

DESCRIPTION

This course explores the integration of human-computer interaction (HCI) research with advanced artificial intelligence systems to design next-generation interactive experiences. Students will learn to develop immersive 3D environments (Unity/VR/Metaverse) while embedding AI-driven capabilities—such as LLM for adaptive agent behaviors, multi-agent coordination, and natural language dialogue systems—into immersive VR systems. The curriculum emphasizes a human-centered methodology, including formative-summative evaluation, ethical alignment, and summative study to ensure the design could satisfy user needs. Meanwhile, the course will also allow students to learn techniques such as motion capture, and AIGC-assisted content, AI agent during system implementation. Students will gain hands-on practice in architecting intelligent systems that balance technical implementation with user-centric design.

AIAA 4435 - Interactive AI Systems: Design & Implementation (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6565)	Fr 03:00PM - 05:50PM	Rm 221, W1	WANG, Yuyang	30	0	30	0

COURSE INFO

CO-REQUISITE	DLED4020
DESCRIPTION	This course is an independent, one-year-long final-year study or project on Artificial Intelligence. A written report, presentation, and/or examination are required. In the final year, via project-related activities, students will learn what AI research is in academic or industrial settings, through reference searching, independent critical thinking, experimentations, academic writing, and presentation, under the supervision or co-supervision of AI faculty members. Students will also practice their English communication skills (reading, writing, understanding, and presentation) in an academic research environment. Credit loads will be spread over the year and can be registered for AI students in their last year of undergraduate study. One of the FYP examination criteria will be whether the projects use AI-assisted programming. Graded: Letter grade or PP. Instructor's approval is required for enrollment.

CO-REQUISITE

DLED4020

DESCRIPTION

This course is an independent, one-year-long final-year study or project on Artificial Intelligence. A written report, presentation, and/or examination are required. In the final year, via project-related activities, students will learn what AI research is in academic or industrial settings, through reference searching, independent critical thinking, experimentations, academic writing, and presentation, under the supervision or co-supervision of AI faculty members. Students will also practice their English communication skills (reading, writing, understanding, and presentation) in an academic research environment. Credit loads will be spread over the year and can be registered for AI students in their last year of undergraduate study. One of the FYP examination criteria will be whether the projects use AI-assisted programming. Graded: Letter grade or PP. Instructor's approval is required for enrollment.

AIAA 4490 - Final Year Capstone Project (6 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6566)	TBA	No room required	TBA	100	0	100	0	Instructor Consent Required

COURSE INFO

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 (6023)	Mo 03:00PM - 05:50PM	Lecture Hall C	CHEN, Yingcong	160	0	160	0

COURSE INFO

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 (6024)	Mo 09:00AM - 11:50AM	Rm 122, E1	DAI, Enyan	60	0	60	0

COURSE INFO

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 (6025)	We 06:00PM - 08:50PM	Rm 102, E4	ZHONG, Bingzhuo	80	0	80	0

COURSE INFO

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.

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 (6026)	Th 01:30PM - 04:20PM	Rm 228, E2	RIKOS, APOSTOLOS	80	0	80	0

COURSE INFO

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.

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 (6027)	Fr 03:00PM - 05:50PM	Rm 134, E1	YUE, Yutao	60 Quota/Enrol/Avail PhD (AI): 20/0/20 PhD (INTR) and IIP (INTR): 20/0/20	0	60	0

COURSE INFO

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 (6028)	Th 06:30PM - 09:20PM	Rm 147, E1	HU, Xuming	40	0	40	0

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 (6073)	TBA	No room required	TBA	999	0	999	0	Instructor Consent Required

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 (6074)	TBA	No room required	TBA	999	0	999	0	Instructor Consent Required

COURSE INFO

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 (6030)	We 11:00AM - 11:50AM	Lecture Hall B	CHEN, Yingcong	175	0	175	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 (6064)	TBA	No room required	TBA	999	0	999	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 (6072)	TBA	No room required	TBA	999	0	999	0