科目概览 (2026/27学年)

This course aims to provide a comprehensive overview of the concepts, technologies, and applications that define smart cities. The primary objective is to equip students with the foundational knowledge and skills necessary to understand and engage with the multifaceted challenges and opportunities that urban environments have faced in recent years. The course will provide a solid foundation for students to engage with and contribute to the development of smart cities, preparing them for further study or careers in this dynamic and impactful field. By the end of the course, students should be able to explain the concept of smart cities and their significance, explain some core technologies that enable smart city solutions, understand the challenges of urbanisation and propose smart solutions, recognise the importance of policy, governance, and citizen engagement, have knowledge on real-world smart city applications, understand the impact of smart cities on sustainability and resilience, anticipate future trends and innovations in the field of smart cities. The course is focused on an overview of smart city technologies, examples and real-world applications. 

This course covers the development and application of mathematical models, statistical analyses, and optimisation techniques to solve complex problems in various industries such as logistics, finance, healthcare, and manufacturing. The course begins with an introduction to the foundational concepts of operational research, including linear programming, integer programming, and network models. Students learn to formulate real-world problems as mathematical models and use various algorithms to find optimal or near-optimal solutions. Key topics often include the simplex method, duality theory, sensitivity analysis, and transportation and assignment problems. In addition to mathematical modelling, the course emphasises the use of software tools like MATLAB, Excel Solver, and specialised OR software to implement and solve models. 

This is a specialised course designed to equip students with the skills and knowledge needed to analyse and interpret complex urban data. The course focuses on the application of data science techniques to address urban challenges, enhance city planning, and improve public services. By the end of the course, students are expected to understand various methods for collecting urban data, including sensors, Internet of Things devices, and public datasets, and be able to manage and preprocess large datasets for analysis.

This course provides a comprehensive understanding of smart connectivity and information fusion in industrial and urban settings. Students will learn about the principles and techniques of integrating data from multiple sources to enhance decision-making and operational efficiency. The curriculum covers the fundamentals of information confusion and Industrial Internet of Things (IIoT) methods, emphasising their applications in various industrial domains. Real-world case studies will illustrate how these technologies can be leveraged to optimise processes, improve system reliability, and drive innovation.

This course provides students with the chance to demonstrate innovative abilities and initiatives in society's problems. Students will be required to carry out group work on a major project under the supervision of individual staff members. The learning process can be theoretical that students are equipped with the essential knowledges and practical in terms of innovative design on project implementing in the real world. The course develops the capability to integrate and apply data science knowledge and data analytical skills to different challenges and scenarios in smart cities. The course also serves as a platform for presenting and sharing novel investigations of academic and/or industrial problems in the real world. 

This course focuses on integrating advanced technologies to enhance urban logistics and supply chain management. The course covers Internet of Things (IoT), AI, and data analytics applications for real-time tracking, efficient routing, and resource optimisation. Students will explore smart warehousing, autonomous delivery systems, and sustainable practices. Through case studies and practical projects, they will learn to design and implement innovative logistics solutions that improve efficiency, reduce costs, and minimise environmental impact, preparing them to tackle the complex challenges of urban logistics in smart cities. 

Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, techniques, and application systems for simulating and extending human intelligence. AI techniques and models have been widely employed in various domain-specific applications due to their promising performance compared to conventional methods. This course focuses on fundamental concepts, techniques, and potential business applications of artificial intelligence. The course provides an overview of waves of AI, intelligent agents, problem-solving, planning, reasoning, learning. It includes topics about search, logic, genetic algorithms, computational learning methods, and some potential business applications like expert systems, news analysis, and so on.

This course introduces how data science techniques are integrated with geographic information systems (GIS) to analyse spatial and temporal data. Students learn to leverage tools like Python, R, and specialised GIS software to perform spatial analysis, visualise geospatial data, and derive insights for various applications, including urban planning, environmental monitoring, and location-based services. The curriculum covers topics such as spatial data acquisition, geostatistics, machine learning for spatial data, and remote sensing. Through hands-on projects and case studies, students gain practical skills in managing and interpreting geospatial datasets to solve real-world problems. 

This course focuses on the deployment and utilisation of sensor technologies to monitor and manage urban environments. The course introduces the applications of various types of sensors, including those for air quality, traffic, noise, and energy consumption. Students learn to integrate these sensors into IoT networks, analyse the collected data, and apply it to enhance urban living, being prepared to develop and implement smart solutions for real-world challenges in urban settings. 

This course focuses on the principles and practices of designing sustainable, liveable urban environments. It covers key topics such as spatial planning, landscape architecture, public space design, and the integration of smart technologies. Students learn to create urban layouts that enhance functionality, aesthetics, and environmental sustainability. The course also explores case studies, digital tools, and collaborative design processes to address contemporary urban challenges. Emphasis is placed on innovative solutions that improve quality of life and foster resilient, adaptable urban spaces in the context of smart city development. 

This course delves into the principles, techniques, and applications of Artificial Intelligence (AI) for creating intelligent agents for playing various types of publicly available digital games and for designing new games. Students will gain hands-on experience with state-of-the-art AI algorithms and frameworks, learning how to design, implement, and evaluate AI systems for gameplay and game design.

This course introduces the fundamental concepts and techniques of 3D computer graphics, focusing on modeling, texturing, rigging, animation, and rendering. Students will learn the industry-standard software and creative workflows used to produce high-quality 3D assets and animated sequences for various applications, including film, games, and visualization. 

This course provides an understanding of the concept and challenge of big data. The focus is on the data analytic techniques to tackle the V’s (volume, velocity, variety, veracity, valence, and value) in big data and how these impact data collection, monitoring, storage, analysis and reporting. The following topics across the big data domain will be introduced: distributed file systems; big data analysis techniques; high-performance processing algorithms for big data; and big data search and query technologies. An example (Apache Spark) of a big data management system to manage and process large-scale data is introduced in the course. Big data analytics applications in business will also be elaborated. Students will actively participate in the delivery of this course through assignments, portfolio development, and projects.

This course equips students with the skills to transform complex industrial data into clear, actionable insights through advanced visualisation and strategic communication techniques. Students will learn to design intuitive, impactful visualisations for datasets such as predictive maintenance logs, supply chain flows, and energy consumption patterns. Hands-on labs using tools like Tableau, Qlikview, Power BI, and Python libraries or D3.js will provide practical experience in solving real-world industrial challenges. The course culminates in a group project where students craft and deliver a compelling data-driven narrative, demonstrating their ability to communicate insights effectively to diverse stakeholders and align analytics with organisational strategy. This course is offered in regular mode. In addition, all required laboratory resources are fully available, and students are expected to possess fundamental self-directed learning capabilities.

Healthcare analytics transform the traditional medical system in an all-round way, making healthcare more efficient, more convenient, and more personalised. This course will introduce students to the key technologies that support smart healthcare. It explains how to build the surveillance infrastructure and how the data is collected and transmitted back from various wearable sensors of multiple sources, by using the technologies of the Internet of Things (IoT): MAC protocols, and routing protocols. This course will also describe data fusion of health and healthcare data, data models, data management, machine learning algorithms, and analytics techniques and tools for health risk prediction. Case studies and examples of applications will be elaborated in this course. 

Since its inception in 2008, blockchain technology has grown beyond its roots in cryptocurrencies to become a transformative force across multiple industries. This course provides a foundational understanding of blockchain principles and explores its practical applications in domains such as the Internet of Things (IoT), construction, and beyond. Through real-world case studies, hands-on assignments, and project-based learning, students will analyse how blockchain is reshaping business models, enhancing transparency, and driving innovation across various sectors. This course is offered in regular mode.

Deep learning is one of the bleeding-edge technologies of machine learning. It is a neural network used to establish and simulate the human brain for analytical learning and to interpret data by imitating the mechanism of the human brain. Deep learning is widely used in computer vision, speech recognition, natural language processing, and other fields. This course aims to provide an intensive understanding and hands-on experience of the existing deep learning approaches. The topics will cover how to select deep neural networks, how to design deep neural networks, and how to train and optimise neural networks for practical applications. The course will cover deep neural network models, including convolutional neural networks, recurrent neural networks, long short-term memory networks, deep residual networks, generative adversarial networks, attention-based models, adversarial learning models, and training techniques including dropout, batch normalisation, selection of activation functions and so on. TensorFlow, PyTorch, or other state-of-the-art deep learning tools will be introduced and applied to solve different classes of problems with huge datasets in business domains. 

Mobile Edge Computing (MEC) is an emerging technology that extends cloud capabilities to the network edge. This course will introduce students to the key concepts, architectures and enabling technologies of MEC. Students will learn about the motivation for MEC and the challenges it addresses in mobile cloud computing. Fundamentals of MEC frameworks and deployment models will be covered. Enabling 5G networking and edge infrastructure technologies facilitating MEC will be examined. Applications such as IoT, augmented reality and smart cities will also be explored. Techniques for offloading tasks, computation partitioning and managing resources at the edge will be studied. Topics including edge artificial intelligence and data processing frameworks will be discussed. Security, privacy preservation and open source/industry edge platforms will be topics of focus. Students will gain an understanding of MEC frameworks for developing low-latency applications. Through this practical course, students will learn the foundations of the impactful new Mobile Edge Computing field. 

The aim of this course is to introduce students to the fundamentals of Python, a general-purpose programming language widely used in the application of Data Science, Big Data Analytics and Optimisation to business problems. The course will provide students the skills for implementing your own algorithms as well as using the thousands of Python packages available for data analysis like modelling and decision support. The lab classes will provide opportunity for students to practice their programming skills and obtain formative feedback. The course is focused on practical knowledge, examples and real-world applications for data analytics. The course is very much hands- on with the ultimate goal of turning students into a versatile data analyst for real-world applications.