Modelling and Analysis for AI

This subject builds up the fundamentals for modelling dynamical systems, with a key focus on the aspects and decisions of modelling that are relevant for the application of AI and data-intensive learning methods. The discussion and evaluation of modelling methods focuses on how model fidelity influences simulation-to-real transfer; how modelling and simulation decisions influence computation time required for training and validation; and how discrete-time models introduce complexity when representing continuous-time engineering systems. Subsequently, it introduces the basic principles and engineering applications of programming and data structures in a condensed form with a project-centric pedagogy. It covers the fundamentals of databases and data structures, basic algorithms, scientific programming, and classic AI problem solving. It will focus specifically on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how programming and databases play a role in modern engineering and cyber-physical systems.