COURSE INFO
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6363) 01-SEP-2025 - 05-SEP-2025
Tu 09:00AM - 09:50AM		Lecture Hall BDAI, Enyan15515050
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 BZHONG, Bingzhuo
15-SEP-2025 - 19-SEP-2025
Tu 09:00AM - 09:50AM		Lecture Hall BWANG, Xin
22-SEP-2025 - 26-SEP-2025
Tu 09:00AM - 09:50AM		Lecture Hall BXIE, Sihong
29-SEP-2025 - 11-OCT-2025
Tu 09:00AM - 09:50AM		Lecture Hall BYANG, Menglin
13-OCT-2025 - 17-OCT-2025
Tu 09:00AM - 09:50AM		Lecture Hall BCHEN, Yingcong
20-OCT-2025 - 24-OCT-2025
Tu 09:00AM - 09:50AM		Lecture Hall BLIANG, Junwei
27-OCT-2025 - 31-OCT-2025
Tu 09:00AM - 09:50AM		Lecture Hall BCHEN, Changhao
03-NOV-2025 - 07-NOV-2025
Tu 09:00AM - 09:50AM		Lecture Hall BXIE, Zeke
10-NOV-2025 - 14-NOV-2025
Tu 09:00AM - 09:50AM		Lecture Hall BYUE, Yutao
17-NOV-2025 - 21-NOV-2025
Tu 09:00AM - 09:50AM		Lecture Hall BLIU, Li
24-NOV-2025 - 28-NOV-2025
Tu 09:00AM - 09:50AM		Lecture Hall BHU, Xuming
01-DEC-2025 - 05-DEC-2025
Tu 09:00AM - 09:50AM		Lecture Hall BRIKOS, APOSTOLOS
COURSE INFO
PRE-REQUISITEUFUG 2601 OR UFUG 2602
DESCRIPTIONThe 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6364) TuTh 01:30PM - 02:50PMRm 102, E4LIU, Li1009370 
L02 (6365) TuTh 04:30PM - 05:50PMRm 101, E1DAI, Enyan9367260 
COURSE INFO
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6366) Fr 09:00AM - 11:50AMRm 149, E1HU, Xuming505005 
L02 (6367) Fr 01:30PM - 04:20PMRm 149, E1HU, Xuming505002 
COURSE INFO
PRE-REQUISITEUFUG 2601 OR UFUG 2602
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6368) Th 09:00AM - 11:50AMRm 148, E1QIN, Chengwei5039110 
L02 (6369) Tu 09:00AM - 11:50AMRm 148, E1SHU, Yao504910 
COURSE INFO
PRE-REQUISITEUFUG 2601 OR UFUG 2602 OR DSAA 1001
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6370) Mo 06:00PM - 08:50PMRm 102, W1CHEN, Jintai5020300 
L02 (6371) We 06:00PM - 08:50PMRm 102, W1LIU, Hao504640 
COURSE INFO
PRE-REQUISITEDSAA 2011
DESCRIPTIONLearning 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6399) Th 03:00PM - 05:50PMRm 122, E1GONG, Zijun
WANG, Xin		5027230 
L02 (6400) TBATBATBA0000
Class cancelled
 
COURSE INFO
PRE-REQUISITEUFUG1103 AND UFUG2104
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6679) Mo 01:30PM - 04:20PMRm 149, E1LIANG, Junwei5032180 
L02 (6680) We 01:30PM - 04:20PMRm 149, E1CHEN, Changhao5013370 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6001) We 09:00AM - 11:50AMRm 102, E4YANG, Menglin605554 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6002) Tu 09:00AM - 11:50AMRm 102, E4CHEN, Huangxun8076410 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6003) Th 09:00AM - 11:50AMRm 101, E1LIU, Li8080021 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONArtificial 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6004) Fr 09:00AM - 11:50AMRm 201, W2XIE, Sihong5033171 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6005) Mo 01:30PM - 04:20PMRm 202, W4BAI, Ge
WANG, Xin		4023171 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6006) Th 01:30PM - 04:20PMRm 202, W4HU, Xuming4040041 
COURSE INFO
VECTOR[1credit]
DESCRIPTIONAn independent research project carried out under the supervision of a faculty member. Graded P or F.

AIAA 6091B - Independent Study (1 unit)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6688) TBATBATBA204160
Instructor Consent Required
 
COURSE INFO
VECTOR[0-1-0:1]
DESCRIPTIONSeries 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
T01 (6007) 01-SEP-2025 - 12-SEP-2025
Fr 01:30PM - 02:20PM		Lecture Hall ADAI, Enyan17417040 
15-SEP-2025 - 05-DEC-2025
Fr 01:30PM - 02:20PM		Lecture Hall CDAI, Enyan
COURSE INFO
DESCRIPTIONMaster'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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6008) TBANo room requiredTBA999929070 
COURSE INFO
DESCRIPTIONOriginal 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6010) TBANo room requiredTBA999289710