COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONMachine learning has emerged as a powerful tool for tackling various problems in engineering and science. Typically via the use of large volume of data, deep neural nets can be trained for this end. However, for engineering and science problems, big data is not enough, and is not always available. This course will introduce the newly emerged paradigm and research trend called “physics-informed machine learning”, where physical laws or physical prior knowledge can be enforced into the architecture of machine learning models, to boost the training and promote the trained models to be more physically consistent and generalizable. At the end of the course, students are expected to understand the principles and methods of physics-informed machine learning, and to have hands-on implementations with Python.

IOTA 5004 - Introduction to Physics-informed Machine Learning (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6041) Tu 09:00AM - 11:50AMRm 202, W2LAI, Zhilu2510150 
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 (6047) Mo 03:00PM - 05:50PMRm 223, W1GONG, Zijun259160 
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 (6042) Fr 10:30AM - 01:20PMRm 233, W1TSANG, Hin Kwok408320 
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 (6043) 22-JAN-2024 - 14-MAR-2024
Th 09:00AM - 11:50AM		Rm 149, E1KUTSCHER, Dirk308220 
15-MAR-2024 - 10-MAY-2024
Th 09:00AM - 11:50AM		Rm 202, E4KUTSCHER, Dirk
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 (6211) Th 04:30PM - 07:20PMRm 222, W1CHEN, Huangxun307230 
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 (6046) Tu 09:00AM - 11:50AMRm 202, E4CUI, Ying206140 
COURSE INFO
VECTOR[3-0-0:3]
PREVIOUS CODEIOTA 6910A
DESCRIPTIONThis course covers fundamental aspects of cybersecurity and privacy. The course will equip students with the ability to understand and analyze security technologies. This course will then explain how these technologies are used and deployed in practical real-world environments, before exploring how recent attacks have discovered new vulnerabilities. The course will emphasize the value of empirical observations and give students insight into how these vulnerabilities can be measured in-the-wild. This course is ideal for students who are interested in understanding cybersecurity risks in a broad range of technologies.

IOTA 5503 - Cybersecurity and Privacy (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6044) Fr 01:30PM - 04:20PMRm 202, W4CHEN, Huangxun
TYSON, Gareth John		309210 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6045) Mo 09:00AM - 11:50AMRm 222, W1HU, Guobiao308220 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course introduces the fundamentals and advanced topics of statistics from its modeling, analysis and inference that covers multivariate distribution along with dimension-reduction techniques, concentration inequalities, classification/clustering, to applications in statistical signal processing, including estimation theory and methods, detection theory and methods, and advanced algorithms such as Markov Chain Monte Carlo (MCMC) and expectation maximization (EM), etc.

IOTA 5505 - Statistics for Inference, Learning and Data Processing (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6212) 22-JAN-2024 - 14-MAR-2024
Tu 03:00PM - 05:50PM		Rm 122, E1GONG, Zijun
XING, Hong		406340 
15-MAR-2024 - 10-MAY-2024
Tu 03:00PM - 05:50PM		Rm 101, W2GONG, Zijun
XING, Hong
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 (6048) Fr 09:30AM - 10:20AMRm 150, E1XING, Hong403190 
COURSE INFO
VECTOR[1-3 credit(s)]
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 (6186) TBATBATBA206140
Instructor Consent Required
 
COURSE INFO
VECTOR[2-1-0:3]
DESCRIPTIONSensor fusion in Wireless Sensor and Actuator Networks (WSANs) is important, particularly in the context of advancing technology and the growing Internet of Things (IoT) landscape. This course offers a comprehensive guide on how to implement sensor fusion filters for processing various types of sensor data. The course is structured to build understanding progressively, starting from basic concepts to more complex implementations. It combines theoretical explanations with practical examples and code snippets in Python, thus bridging the gap between theory and practical application in sensor data processing and analysis.

IOTA 6910E - Sensor FusionIn Wireless Sensor & Actuator Networks (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6337) 22-JAN-2024 - 14-MAR-2024
Tu 01:30PM - 03:20PM		Rm 103, E1CHANG, Tengfei209110 
15-MAR-2024 - 10-MAY-2024
Tu 01:30PM - 03:20PM		Rm 201, E3CHANG, Tengfei
T01 (6338) 22-JAN-2024 - 14-MAR-2024
Tu 03:30PM - 04:20PM		Rm 103, E1CHANG, Tengfei209110 
15-MAR-2024 - 10-MAY-2024
Tu 03:30PM - 04:20PM		Rm 201, E3CHANG, Tengfei
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 (6049) TBATBATBA99979920 
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 (6050) TBATBATBA99939960