Data-driven decision-making is becoming a crucial skill in dealing with engineering systems that generate vast amounts of data from the automation system. Data science is improving our understanding of complex phenomena even faster than physical models have done in the past. Engineering Systems are composed of many complex elements, and their mutual interaction is not trivial to evaluate and predict by adopting the conventional first principles physics-based models.

This course will exploit advanced statistical techniques to build models directly based on the large amount of historical data collected by the recently advanced automation systems without prior knowledge of the underlying physical system. The course will focus on Artificial Intelligence (AI), Machine Learning (ML), and Data-driven models (DDMs) for engineering applications, including linear and non- linear models, shallow and deep models, and the best practices for model selection and error estimation. Numerical examples and real-life problems will be proposed and analysed, from bearings fault prediction, to fuel consumption optimisation. All course material will be freely available in PDF format for a complete understanding of the related subjects as well as for future consultation. During the afternoon session, hands-on workshops will be delivered with numerical examples focused on various aspects of AI, ML, and DDMs.

The course is designed for students interested in data analysis with an engineering background, numerical skills, and a rudimentary understanding of statistics. This course covers methodologies necessary for inferring useful information and identifying underlying patterns from raw, incomplete, noisy, and corrupted data that is present in real-life engineering applications. This is achieved by introducing concepts and methods used to model a wide range of systems based on available data.