Course Descriptions (AY2026/27 Intake)

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 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 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 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 a balanced blend of control systems theory and practical data analytics in industrial settings. Students will learn about modeling, analysis, and design techniques for dynamic systems, gaining a solid foundation in both classical and modern control strategies. The curriculum emphasises the importance of feedback mechanisms and stability analysis, enabling participants to effectively design and implement control systems tailored to various applications. In addition to control theory, the course delves into industrial analytics, highlighting the significance of system identification and data-driven decision-making in optimising industrial process control. Real-world case studies will illustrate how analytics can be applied to enhance operational efficiency, reduce downtime, and drive innovation. 

This course provides students the chance to demonstrate innovative abilities and initiatives for solving data analytics problems in a specific industrial sector such as manufacturing, energy, transportation, logistics, and healthcare. Students will be required to carry out independent work on a major project, which can be theoretical or practical, under the supervision of individual staff members. The course develops the capability to integrate and apply data analytical knowledge and skills to different scenarios. The course also serves as a platform for presenting and sharing novel investigations of academic and/or industrial problems in the real world involving industrial data analytics. 

This course provides a comprehensive introduction to embodied intelligence, emphasising 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 optimising 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 provides a foundational understanding of Digital Twin technology, an emerging paradigm that integrates physical systems with their virtual counterparts to enable real-time monitoring, simulation, and optimisation. Students will master state-of-the-art techniques for building and deploying digital twins, leveraging real-time data streams, predictive analytics, and AI-driven simulations to enhance industrial decision-making. The curriculum integrates cross-domain synergies between IoT, edge computing, and industrial metaverse frameworks, emphasising applications in smart manufacturing. Through hands-on projects and industrial case studies, learners will gain practical skills in designing scalable digital twin solutions that drive efficiency, reduce costs, and unlock new paradigms in Industry 4.0 innovation. 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 provides an in-depth exploration of large language models (LLMs), focusing on their principles, architectures, training methodologies, and applications. Students will learn about the evolution of LLMs, from early models to state-of-the-art systems like GPT-4. The course covers key concepts such as natural language processing, transfer learning, and ethical considerations in AI. Through hands-on projects and case studies, students will gain practical experience in developing and deploying LLMs across various domains. 

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.

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.

Healthcare analytics transform the traditional medical system in an all-around 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 Internet of Things (IoT): Medium Access Control (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.

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 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.