COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course will introduce students to the fundamentals of Social and Web Computing, as well as providing detailed coverage of recent research in this space. It will consist of two major parts. First, students will learn about fundamental social computing theories, alongside computational methodologies that can be used to understand human interactions online. Second, students will be exposed to a range of recent applied research that has employed these methodologies. There will be an empirical focus in the course, and students will be exposed to a range of measurement research capturing how social and web systems work in-the-wild. Students will learn about social network analysis and relevant APIs, alongside related aspects of the Web, user privacy and online advertisement.

IOTA 5001 - Social and Web Computing (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6099) Mo 01:30PM - 04:20PMRm 221, W1TYSON, Gareth John3010200 
COURSE INFO
VECTOR[3-0-0:3]
EXCLUSIONCMAA 5017
CO-LIST WITHCMAA 5017
DESCRIPTIONThis course introduces students to the concepts, theories, and interaction techniques in AR/VR/MR/XR. It covers both the fundamental concepts and design theories and the state-of-the-art interaction techniques in the field. In addition, students will work independently or in teams to design, develop, and evaluate AR/VR/MR/XR applications. In sum, by the end of the class, students will have a solid grasp of the fundamental concepts and theories and hands-on experience in AR/VR/MR/XR.

IOTA 5009 - AR/VR/MR/XR: Concepts, Theory, and Techniques (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6396) Mo 01:30PM - 04:20PMTBAFAN, Mingming204160
The classroom is W2201
 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course introduces students to the fundamentals and latest research on localization technologies, including GPS, indoor positioning based on ultra-wideband communications, and simultaneous localization and communications in 5G/6G, et al. Apart from electromagnetic waves, localization based on acoustic signals will also be introduced. The course will provide students with the fundamental knowledge required to understand, analyze and develop localization technologies.

IOTA 5102 - Fundamentals of Localization Technologies (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6115) TBATBATBA300300 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course introduces students to different types of communication networks, with a focus on Internet technologies. Topics covered include error control, flow control, medium access control, routing, congestion control, packet scheduling, queueing theory, and network optimization. The course will provide students with the fundamental knowledge required to understand, analyze, and optimize the performance of communication networks. It is suitable for students who do not have an existing background in communication networks.

IOTA 5106 - Introduction to Communication Networks (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6101) Fr 10:30AM - 01:20PMRm 202, E3TSANG, Hin Kwok405350 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course focuses on relevant current research topics in Networked Systems such as Networking for AI, Multimedia Communication, Data-Center Networks, Software-Defined Networking, Dataplane Programmability, Information-Centric Networking, Quantum Internet Communication, Privacy Preserving Communication, Constrained (IoT) Networks. This course will provide a structured introduction to the state-of-the-art and recent research in the field of networked systems. It will further introduce students to recent development efforts in the academic community as well as in Internet standardization.

IOTA 5107 - Advanced Networked Systems for AI and Multimedia Systems (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6120) Mo 09:00AM - 11:50AMRm 101, W4KUTSCHER, Dirk305250 
COURSE INFO
VECTOR[2-1-0:3]
DESCRIPTIONThis course teaches the basic concepts of wireless sensor and actuator networks (WSANs), and how swarm intelligence is applied over WSAN. The course content includes the typical architecture of the hardware WSAN devices, the general communication mechanism and examples of applications using WSANs to realize swarm intelligence.

IOTA 5109 - Wireless Sensor and Actuator Networks Toward Swarm Intelligence (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6336) TBATBATBA400400 
T01 (6337) TBATBATBA400400 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course explores cutting-edge techniques for creating efficient machine-learning models to address the growing demand for real-time decision-making and localized processing across diverse application fields, including IoT/robotics/smart manufacturing systems and beyond. Key topics include model compression, pruning, quantization, neural architecture search, knowledge distillation, on-device fine-tuning, transfer learning, application-specific acceleration techniques, etc. Through hands-on projects, students will learn to optimize and adapt deep learning models for resource-constrained devices while maintaining accuracy and performance.

IOTA 5202 - Efficient Machine Learning for Resource Constrained Environments (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6108) Tu 01:30PM - 04:20PMRm 102, W1CHEN, Huangxun302460 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course covers advanced theory, algorithms, and applications for convex and nonconvex optimization, including: subgradient methods, localization methods, decomposition methods, proximal methods, alternating direction methods of multipliers, conjugate direction methods, successive approximation methods, convex-cardinality problems, low-rank optimization problems, neural networks, semidefinite programming and relaxation, robust optimization, discrete optimization, stochastic optimization, etc.

IOTA 5502 - Convex and Nonconvex Optimization II (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6110) Mo 03:00PM - 05:50PMRm 101, E4CUI, Ying205150 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course introduces the fundamentals and advanced topics of statistics that cover multivariate distribution along with dimension-reduction techniques, concentration inequalities, performance metrics for statistical learning, and applications in statistical signal processing, including estimation theory and methods, detection theory and methods, and selected advanced algorithms such as Markov Chain Monte Carlo (MCMC) and expectation maximization (EM), etc.

IOTA 5505 - Statistical Inference for Machine Learning and Signal Processing (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6125) Tu 06:00PM - 08:50PMRm 222, W1GONG, Zijun
XING, Hong		3010200 
COURSE INFO
VECTOR[0-1-0:1]
DESCRIPTIONA 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. Continuation of IOTA 6101. Graded P or F.

IOTA 6102 - Internet of Things Seminar II (1 unit)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
T01 (6338) Tu 04:30PM - 05:50PMRm 228, E2CHEN, Huangxun504640 
COURSE INFO
VECTOR3 credits
DESCRIPTIONAn independent research project carried out under the supervision of a faculty member on an Internet of Things topic.

IOTA 6900A - Independent Study (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6773) TBATBATBA200200
Instructor Consent Required
 
COURSE INFO
VECTOR[1 credit]
DESCRIPTIONAn independent research project carried out under the supervision of a faculty member on an Internet of Things topic.

IOTA 6900B - Independent Study (1 unit)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6774) TBATBATBA202180
Instructor Consent Required
 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course introduces the fundamentals of information theory and its applications in machine learning and statistics. Beyond compression and transmission, information theory also addresses information extraction and learning. By formulating operational problems with elegant mathematics and solving them through powerful techniques, it characterizes fundamental performance limits and offers deep insights into system design. We will cover both classical and modern topics. The course begins with core information measures—entropy, f-divergences, convex duality, and variational characterizations—followed by data compression, channel coding, and strong data-processing inequalities, which illustrate the operational meaning of these definitions. We then explore information-theoretic treatments of inference, hypothesis testing, and large deviations. Applications include deriving generalization bounds, and inequalities in machine learning, as well as insights for statistical estimation. By the end of the course, students will gain both the tools and intuition to analyze fundamental limits and to apply information-theoretic methods to modern problems in learning and inference.

IOTA 6910I - Information Theory and its Applications in Machine Learning (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6747) Th 01:30PM - 04:20PMRm 222, W1HE, Haiyun4030100 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course will teach students advanced methods to measure, model, and protect the security and privacy of sensing technologies and their users in the age of AI and IoT, with an emphasis on the hardware-software interface in embedded systems and the physics of sensing and computation. Application domains of security analysis include smartphone and AR/VR devices, smart home IoT, autonomous driving, healthcare and medical devices, AI-based controls and Deepfakes, etc. Group projects will have students build voice assistant systems from scratch and explore various offensive and defensive security techniques.

IOTA 6910J - Sensing Security and Privacy in IoT and AI Systems (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6757) We 09:00AM - 11:50AMRm 148, E1LONG, Yan4014260 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONFrom operating systems to artificial intelligence, the principles of distributed computing underpin much of contemporary computer science. This course provides a comprehensive introduction to distributed systems and their applications in distributed learning. The first part focuses on foundational concepts in distributed computing, covering classical problems such as synchronization, fault tolerance, and communication protocols, as well as the theoretical challenges that arise when computation and communication are distributed across multiple nodes. Building on this foundation, the second part explores how distributed principles are applied to large-scale machine learning. Topics include federated learning, large language models (LLMs), and distributed AI agent coordination, highlighting how distributed systems continue to evolve and shape the future of intelligent computing.

IOTA 6910K - Distributed and Federated Machine Learning (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6749) We 12:00PM - 02:50PMRm 201, W2SU, Ningxin302190 
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.

IOTA 6990 - MPhil Thesis Research (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6025) TBANo room requiredTBA999189810 
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.

IOTA 7990 - Doctoral Thesis Research (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6027) TBANo room requiredTBA999359640