IOTA - HKUST Class Schedule & Quota

COURSE INFO

DESCRIPTION	Data-driven modeling is revolutionizing the modeling and predicting of complex systems. This cross-disciplinary course will introduce methodologies for integrating time-series analysis, machine learning, engineering mathematics, and mathematical physics, into data-driven methods for inferring and building models from data. At the end of the course, students are expected to understand the principles and methods of extracting patterns and models from data and making effective predictions, and to have hands-on implementations with Python/Matlab. In-class lab demonstrations will also be provided.

DESCRIPTION

Data-driven modeling is revolutionizing the modeling and predicting of complex systems. This cross-disciplinary course will introduce methodologies for integrating time-series analysis, machine learning, engineering mathematics, and mathematical physics, into data-driven methods for inferring and building models from data. At the end of the course, students are expected to understand the principles and methods of extracting patterns and models from data and making effective predictions, and to have hands-on implementations with Python/Matlab. In-class lab demonstrations will also be provided.

IOTA 5002 - Data-driven Modeling: Learning from Sensor Data (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6382)	Tu 09:00AM - 11:50AM	Rm 102, E1	LAI, Zhilu	30	10	20	0

COURSE INFO

EXCLUSION	INTR 5220
CO-LIST WITH	INTR 5220
DESCRIPTION	This course aims to develop students’ fundamental understanding of the application scenarios, challenges, and solutions of wireless connectivity in various systems involving autonomous things, and under possible mobility. Topics covered include fundamentals of digital communications, future wireless connectivity requirements, and various solutions to the unique challenges such as dynamic propagation environment, scalability, complexity, and heterogeneity.

IOTA 5003 - Wireless Connectivity for Mobile Autonomous Things (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6516)	Tu 09:00AM - 11:50AM	TBA	YAN, Jia YANG, Liuqing	15	0	15	0	>The classroom is W1102

COURSE INFO

DESCRIPTION	This course introduces students to the fundamentals of discrete-time signal processing, for both linear time invariant (LTI) and non-LTI systems. For LTI systems, the topics include the sampling theorem, Fourier transform, convolution, and spectrum analysis, which lays the foundation for OFDM in wireless communications. Advanced topics will also be covered for time-variant systems, such as Heisenberg transform, Wigner distribution and Fractional Fourier transform, and their applications in radar, sonar and the OTFS modulation in wireless communications.

DESCRIPTION

This course introduces students to the fundamentals of discrete-time signal processing, for both linear time invariant (LTI) and non-LTI systems. For LTI systems, the topics include the sampling theorem, Fourier transform, convolution, and spectrum analysis, which lays the foundation for OFDM in wireless communications. Advanced topics will also be covered for time-variant systems, such as Heisenberg transform, Wigner distribution and Fractional Fourier transform, and their applications in radar, sonar and the OTFS modulation in wireless communications.

IOTA 5104 - Fundamentals of Discrete-Time Signal Processing (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6383)	Th 09:00AM - 11:50AM	Rm 238, E1	GONG, Zijun YANG, Liuqing	30	0	30	0

COURSE INFO

DESCRIPTION	This course lays theoretical foundation for students with general EE background to pursue research advances involving wireless communication-based systems, e.g., 4G/5G mobile communication, IoT and WLAN. In this course, concepts of wireless channels, its modelling as well as channel capacity for point-to-point/multi-user/MMO communications will be systematically introduced. Along with understanding of these theories, the principles and state-of-the-art technologies to combat fading and interference including general diversity techniques, OFDM, and multiple access schemes will be conveyed. Finally, a few advanced topics for 5G-and beyond (B5G) communication system design will be briefly introduced, such as massive MIMO and reconfigurable intelligent surface (RIS).

DESCRIPTION

This course lays theoretical foundation for students with general EE background to pursue research advances involving wireless communication-based systems, e.g., 4G/5G mobile communication, IoT and WLAN. In this course, concepts of wireless channels, its modelling as well as channel capacity for point-to-point/multi-user/MMO communications will be systematically introduced. Along with understanding of these theories, the principles and state-of-the-art technologies to combat fading and interference including general diversity techniques, OFDM, and multiple access schemes will be conveyed. Finally, a few advanced topics for 5G-and beyond (B5G) communication system design will be briefly introduced, such as massive MIMO and reconfigurable intelligent surface (RIS).

IOTA 5105 - Fundamentals of Wireless Communications (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6384)	Th 01:30PM - 04:20PM	Rm 235, E1	XING, Hong	30	11	19	0

COURSE INFO

DESCRIPTION	This course offers a comprehensive introduction to Reinforcement Learning (RL), building a foundation from core concepts like Markov Decision Processes (MDPs) and dynamic programming to advanced algorithms such as policy gradients and actor-critic methods (PPO, DDPG). It progresses to modern topics including Multi-Agent Reinforcement Learning (MARL), making it ideal for students across engineering and science disciplines looking to apply RL in their research.

DESCRIPTION

This course offers a comprehensive introduction to Reinforcement Learning (RL), building a foundation from core concepts like Markov Decision Processes (MDPs) and dynamic programming to advanced algorithms such as policy gradients and actor-critic methods (PPO, DDPG). It progresses to modern topics including Multi-Agent Reinforcement Learning (MARL), making it ideal for students across engineering and science disciplines looking to apply RL in their research.

IOTA 5201 - Reinforcement Learning for Intelligent Decision Making (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6385)	Tu 01:30PM - 04:20PM	Rm 102, W1	YU, Jiadong	40	39	1	0

COURSE INFO

PREVIOUS CODE	IOTA 6910E
DESCRIPTION	Networked robots are increasingly used in exploration, logistics, inspection, and autonomous systems, where teams of robots must perceive their environment, share information, and make coordinated decisions. This course introduces the fundamental principles behind how such robots combine data from multiple sensors, and from one another, to build reliable perception and cooperative intelligence. Students will learn essential concepts in probabilistic sensor fusion, core filtering techniques, and modern machine-learning-based fusion approaches, and explore how these methods enable tasks such as distributed localization, collaborative mapping, and coordinated autonomy. The course provides practical skills for developing intelligent multi-robot systems and offers a strong foundation for research or advanced work in AI, robotics, and autonomous systems.

PREVIOUS CODE

IOTA 6910E

DESCRIPTION

Networked robots are increasingly used in exploration, logistics, inspection, and autonomous systems, where teams of robots must perceive their environment, share information, and make coordinated decisions. This course introduces the fundamental principles behind how such robots combine data from multiple sensors, and from one another, to build reliable perception and cooperative intelligence. Students will learn essential concepts in probabilistic sensor fusion, core filtering techniques, and modern machine-learning-based fusion approaches, and explore how these methods enable tasks such as distributed localization, collaborative mapping, and coordinated autonomy. The course provides practical skills for developing intelligent multi-robot systems and offers a strong foundation for research or advanced work in AI, robotics, and autonomous systems.

IOTA 5203 - Sensor Fusion and Cooperative Intelligence for Networked Robotics (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6387)	Mo 09:00AM - 11:50AM	Room 521H VR Room, W1	CHANG, Tengfei	30	0	30	0

COURSE INFO

DESCRIPTION	This course covers fundamental theory, algorithms, and applications for convex and nonconvex optimization, including: 1) Theory: convex sets, convex functions, optimization problems and optimality conditions, convex optimization problems, geometric programming, duality, Lagrange multiplier theory; 2) Algorithms: disciplined convex programming, numerical linear algebra, unconstrained minimization, minimization over a convex set, equality constrained minimization, inequality constrained minimization; 3) Applications: approximation (regression), statistical estimation, geometric problems, classification, etc.

DESCRIPTION

This course covers fundamental theory, algorithms, and applications for convex and nonconvex optimization, including: 1) Theory: convex sets, convex functions, optimization problems and optimality conditions, convex optimization problems, geometric programming, duality, Lagrange multiplier theory; 2) Algorithms: disciplined convex programming, numerical linear algebra, unconstrained minimization, minimization over a convex set, equality constrained minimization, inequality constrained minimization; 3) Applications: approximation (regression), statistical estimation, geometric problems, classification, etc.

IOTA 5501 - Convex and Nonconvex Optimization I (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6390)	Mo 01:30PM - 04:20PM	Rm 202, E1	CUI, Ying	40	17	23	0

COURSE INFO

DESCRIPTION	This course is an introduction to the algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis. The course will help students develop a basic understanding of numerical algorithms and the skills to implement algorithms to solve mathematical problems on the computer.

IOTA 5504 - Approximate Computing - Introduction to Numerical Analysis (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6391)	Tu 09:00AM - 11:50AM	Rm 205, C7 Library	HU, Guobiao	30	12	18	0

COURSE INFO

DESCRIPTION	A series of regular seminars presented by postgraduate students, faculty, and guest speakers on IoT-related research problems currently under investigation. Students are expected to attend regularly. Graded P or F.

IOTA 6101 - Internet of Things Seminar I (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
T01 (6394)	Fr 01:30PM - 02:20PM	Rm 122, E1	GAO, Shijian LI, Hongyu	50	50	0	0

DESCRIPTION	An independent research project carried out under the supervision of a faculty member on an Internet of Things topic.

IOTA 6900A - Independent Study (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6399)	TBA	No room required	TBA	20	0	20	0	Instructor Consent Required

DESCRIPTION	An independent research project carried out under the supervision of a faculty member on an Internet of Things topic.

IOTA 6900B - Independent Study (1 unit)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6401)	TBA	No room required	TBA	20	2	18	0	Instructor Consent Required

COURSE INFO

DESCRIPTION	This course provides a foundational introduction to microeconomics, game theory, and algorithmic mechanism design, with a specific focus on their application to modern multi-agent systems for AIoT. Students will explore how economic principles and strategic interactions govern the behavior of agents in a variety of networked environments, including adversarial defense, information systems, communication networks, social networks, and cyber-physical systems like energy and transportation grids. The curriculum bridges theoretical concepts with practical applications. Core topics include market mechanisms, consumer preferences, and the objectives of cost and welfare maximization. A significant portion of the course is dedicated to multi-agent game theory, covering strategic games, pure and mixed strategy Nash equilibrium, correlated equilibrium, and the dynamics of repeated games. We will also explore extensive-form games, solution concepts like backward induction and subgame perfect equilibrium, and games of incomplete information, such as Bayesian games. The course then advances to contemporary multi-agent paradigms, examining bandit and reinforcement learning algorithms for sequential decision-making under uncertainty, the principles of federated learning algorithms for privacy-preserving distributed optimization, and their connections to multi-agent mechanism design—including auctions, pricing, contracts, facility location, and information manipulation. The course concludes by tying these elements together within the context of distributed systems that underpin these multi-agent interactions.

DESCRIPTION

This course provides a foundational introduction to microeconomics, game theory, and algorithmic mechanism design, with a specific focus on their application to modern multi-agent systems for AIoT. Students will explore how economic principles and strategic interactions govern the behavior of agents in a variety of networked environments, including adversarial defense, information systems, communication networks, social networks, and cyber-physical systems like energy and transportation grids. The curriculum bridges theoretical concepts with practical applications. Core topics include market mechanisms, consumer preferences, and the objectives of cost and welfare maximization. A significant portion of the course is dedicated to multi-agent game theory, covering strategic games, pure and mixed strategy Nash equilibrium, correlated equilibrium, and the dynamics of repeated games. We will also explore extensive-form games, solution concepts like backward induction and subgame perfect equilibrium, and games of incomplete information, such as Bayesian games. The course then advances to contemporary multi-agent paradigms, examining bandit and reinforcement learning algorithms for sequential decision-making under uncertainty, the principles of federated learning algorithms for privacy-preserving distributed optimization, and their connections to multi-agent mechanism design—including auctions, pricing, contracts, facility location, and information manipulation. The course concludes by tying these elements together within the context of distributed systems that underpin these multi-agent interactions.

IOTA 6910L - Multi-agent Systems: Games, Learning and Economics (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6938)	We 09:00AM - 11:50AM	Rm 205, C7 Library	DUAN, Lingjie	30	18	12	0

COURSE INFO

DESCRIPTION	This course introduces fundamental and advanced machine learning techniques for ubiquitous computing systems, with a focus on sensor-driven human-centric applications. It covers representation learning from multimodal sensor data, self-supervised learning, generative AI, and challenges such as domain shift, personalization, and on-device deployment. Through case studies in wearable AI, mobile computing, and smart cities, students will learn how to design, analyze, and deploy scalable learning systems in real-world ubiquitous settings.

DESCRIPTION

This course introduces fundamental and advanced machine learning techniques for ubiquitous computing systems, with a focus on sensor-driven human-centric applications. It covers representation learning from multimodal sensor data, self-supervised learning, generative AI, and challenges such as domain shift, personalization, and on-device deployment. Through case studies in wearable AI, mobile computing, and smart cities, students will learn how to design, analyze, and deploy scalable learning systems in real-world ubiquitous settings.

IOTA 6910M - Machine Learning for Ubiquitous Computing (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6939)	Tu 09:00AM - 11:50AM	Rm 306, C7 Library	HONG, Zhiqing	30	18	12	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.

IOTA 6990 - MPhil Thesis Research (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6397)	TBA	No room required	TBA	50	16	34	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.

IOTA 7990 - Doctoral Thesis Research (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6398)	TBA	No room required	TBA	50	36	14	0