COURSE CURRICULUM

This course comprises a series of compulsory modules designed to provide a comprehensive foundation in artificial intelligence, data science, and intelligent systems engineering. The Statistical Learning Methods module equips students with practical statistical tools essential for managing uncertainty in real-life data analysis, emphasizing Bayesian methods, regression, and classification models. Search and Optimisation introduces decision theory and modern optimization methods, including linear, non-linear, and multi-criteria techniques, fostering skills to tackle complex decision-making problems. Deep Learning for Computer Vision explores advanced neural network architectures such as CNNs and RNNs, with practical applications in object detection, tracking, and prediction across industries like aerospace and manufacturing. Intelligent Cyber Physical Systems focuses on sensor-actuator networks, embedded systems, robotics, and countermeasures in connected environments. The Data Analytics and Visualisation module presents a workflow for analyzing large datasets using clustering, regression, and visualisation tools to support knowledge discovery. Deep Learning for Autonomous Decision Making covers cutting-edge technologies such as large language models, reinforcement learning, and graph neural networks, allowing students to create solutions for autonomous decision-making systems in real-world contexts. Logic and Automated Reasoning delves into formal languages and reasoning techniques to build automated systems, linking causal inference with machine learning for robust engineering models. Additionally, the Ethical, Regulatory and Social Aspects of AI module ensures students can critically engage with the broader societal implications of AI, addressing the responsible design, deployment, and governance of intelligent systems. Through hands-on labs and case studies, students apply theoretical principles to practical challenges, enhancing their ability to develop, evaluate, and refine AI systems. Upon completion, graduates will possess the analytical, programming, and system design skills needed to innovate across sectors including transport, aerospace, manufacturing, and intelligent infrastructure, with a strong understanding of the ethical and regulatory considerations that shape the future of AI.