| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6099) | Mo 01:30PM - 04:20PM | Rm 221, W1 | TYSON, Gareth John | 30 | 0 | 30 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| EXCLUSION | CMAA 5017 |
| CO-LIST WITH | CMAA 5017 |
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6396) | Mo 01:30PM - 04:20PM | TBA | FAN, Mingming | 20 | 0 | 20 | 0 | The classroom is C7306 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6115) | Fr 03:00PM - 05:50PM | Rm 202, W4 | GONG, Zijun | 30 | 0 | 30 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6101) | Fr 10:30AM - 01:20PM | Rm 202, E3 | TSANG, Hin Kwok | 40 | 0 | 40 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6120) | Mo 09:00AM - 11:50AM | Rm 101, W4 | KUTSCHER, Dirk | 30 | 0 | 30 | 0 |
| VECTOR | [2-1-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6336) | Th 09:00AM - 10:50AM | Rm 222, W1 | CHANG, Tengfei | 40 | 0 | 40 | 0 | |
| T01 (6337) | Th 11:00AM - 11:50AM | Rm 222, W1 | CHANG, Tengfei | 40 | 0 | 40 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6108) | Tu 01:30PM - 04:20PM | Rm 102, W1 | CHEN, Huangxun | 30 | 0 | 30 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6110) | Mo 03:00PM - 05:50PM | Rm 101, E4 | CUI, Ying | 20 | 0 | 20 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6125) | Tu 06:00PM - 08:50PM | Rm 222, W1 | XING, Hong | 30 | 0 | 30 | 0 |
| VECTOR | [0-1-0:1] |
|---|---|
| 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. Continuation of IOTA 6101. Graded P or F. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| T01 (6338) | Tu 04:30PM - 05:50PM | Rm 228, E2 | CHEN, Huangxun | 50 | 0 | 50 | 0 |
| 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. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6025) | TBA | No room required | TBA | 999 | 0 | 999 | 0 |
| 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. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6027) | TBA | No room required | TBA | 999 | 0 | 999 | 0 |