System Optimisation & Machine Learning (ELEN90088)

This subject introduces the basic principles, analysis methods, and applications of optimisation and machine learning to engineering systems; encompassing fundamental concepts and practical algorithms. It covers the fundamentals of continuous optimisation followed by machine learning basics for engineering applications.

The concepts and methods discussed are illustrated in multiple application areas including Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.The concepts taught in this subject will allow a better understanding of continuous optimisation and machine learning for systems engineering.

INDICATIVE CONTENT

Topics covered may include:

• Fundamentals of continuous optimisation: convex sets and functions; local vs global solutions, constrained optimisation and Lagrange multipliers; linear, quadratic, and nonlinear programming
• Basics of machine learning encompassing supervised and unsupervised learning: binary classification, linear and nonlinear regression, kernel methods, and clustering.
• Specific machine learning methods such as Support Vector Machines (SVMs), Neural Networks (NNs), k-means clustering, and reinforcement learning.
• Applications to Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.