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 (6094) Mo 01:30PM - 04:20PMRm 201, E1TYSON, Gareth John4013270 
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 (6082) Tu 09:00AM - 11:50AMRm 223, W1LAI, Zhilu302910 
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 (6083) Fr 03:00PM - 05:50PMRm 201, E1GONG, Zijun309210 
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 (6084) Fr 10:30AM - 01:20PMRm 201, E1TSANG, Hin Kwok407330 
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 (6086) Mo 03:00PM - 05:50PMRm 202, W1CUI, Ying201640 
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 (6087) Mo 09:00AM - 11:50AMRm 150, E1HU, Guobiao3010200 
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 (6088) We 06:30PM - 09:20PMRm 150, E1GONG, Zijun
XING, Hong		309210 
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 (6089) Fr 01:30PM - 02:20PMRm 228, E2CHEN, Huangxun655690 
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 (6519) TBATBATBA203170
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 (6546) TBATBATBA202180
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 (6120) Tu 03:00PM - 04:50PMRm 102, E1CHANG, Tengfei408320 
T01 (6121) Tu 05:00PM - 05:50PMRm 102, E1CHANG, Tengfei407330 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course introduces stochastic processes, focusing on their application in Electrical Engineering, Computer Science, and other related Engineering fields. Stochastic processes are vital for modeling systems affected by uncertainty, which is crucial in areas such as communications, signal processing, control systems. Students will explore mathematical foundations, including probability models, random variables, and statistical inference. Topics such as random vectors, Gaussian processes, and continuous-time random processes will be covered, providing essential tools for analyzing complex data. A key emphasis will be on applications in radar systems, demonstrating how stochastic models improve signal processing and system design. Additionally, students will learn how these concepts apply to IoT, where managing uncertainty in large data sets is critical for enhancing performance and reliability. By bridging multiple engineering disciplines, this course prepares students to tackle real-world challenges, equipping them with skills necessary for innovative solutions in an increasingly data-driven landscape.

IOTA 6910F - Stochastic Process in Engineering Systems (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6095) Tu 06:00PM - 08:50PMRm 102, E1GAO, Shijian308220 
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 (6091) TBANo room requiredTBA999179820 
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 (6093) TBANo room requiredTBA99989910