科目概覽 (2027/28學年)

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 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 optimization 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 emphasizes the use of software tools like MATLAB, Excel Solver, and specialized 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 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. 

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 explores the integration of generative AI tools in learning and applying programming skills. Students will discover how to leverage state-of-the-art generative models to enhance their coding proficiency and develop innovative applications. The curriculum covers foundational programming concepts, practical implementation strategies, and ethical considerations. Through hands-on projects, students will gain experience in using generative AI to assist with coding, debugging, and project development, preparing them for advanced programming challenges.

This course provides a comprehensive introduction to embodied intelligence, emphasizing the integration of sensing, cognition, and autonomous decision-making within industrial contexts. Students will explore state-of-the-art methodologies in multimodal perception, real-time data fusion, and adaptive control, which empower intelligent systems to interact effectively with dynamic physical environments. Key topics include the role of edge computing, human-machine collaboration, and intelligent automation in optimizing industrial workflows, enabling predictive maintenance, and advancing Industry 4.0 innovations. Through hands-on projects and case studies in smart manufacturing, robotics, and autonomous systems, learners will gain practical experience in designing and deploying embodied intelligence solutions that bridge the gap between theoretical models and real-world applications. 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.

This course aims to equip students with the technical mastery and creative mindset to harness generative artificial intelligence as a transformative tool for innovative design. Moving beyond the analytical focus of traditional deep learning, it explores advanced architectures—including Generative Adversarial Networks (GANs), diffusion models, and transformer-based systems—for the synthesis of original visual, multimodal, and conceptual outputs. Students will learn to train, fine-tune, and ethically direct generative models to produce and iterate design prototypes, effectively bridging computational techniques with human-centred creative processes. Through hands-on projects, the course cultivates the ability to integrate generative AI into the design workflow, empowering future innovators to leverage cutting-edge technology in the creation of novel products, experiences, and solutions.

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 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 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 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 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.

This course introduces the principles and practice of intelligent transportation systems (ITS) in the context of smart cities, treating ground and low-altitude aerial mobility as two layers of one integrated, three-dimensional transportation system. From a data science and AI perspective, students will learn how perception, sensing, connectivity and data analytics underpin autonomous driving and the low-altitude economy (e.g., drones and eVTOLs), and how these technologies are coordinated with urban infrastructure and airspace management.

The course further examines how intelligent transportation systems connect with the broader smart-city ecosystem, for example through digital-twin and simulation technologies that link transportation data to city-wide planning and decision-making. Through hands-on coding exercises and a final practical project, students will gain the technical skills to build, prototype and evaluate data-driven models and algorithms for intelligent transportation, supporting the low-altitude economy and the wider development of smart, connected cities.

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. 

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. 

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.