Modern engineering systems generate vast amounts of data through increasingly advanced sensing, automation, and monitoring infrastructures. As a result, data-driven decision-making has become a core competence for engineers who need to understand, predict, and optimise complex systems in real operational conditions. While physics-based (first-principles) modelling remains essential, many real-world engineering problems involve strong nonlinearities, high-dimensional interactions, uncertainties, and limited observability—making purely mechanistic approaches difficult to calibrate and deploy at scale. Data science and machine learning provide complementary tools that can extract patterns and actionable insight directly from measured data, often accelerating understanding and enabling new capabilities.

This summer school introduces Artificial Intelligence (AI), Machine Learning (ML), and Data-Driven Models (DDMs) for engineering applications, spanning linear and non-linear methods, shallow and deep learning models, and best practices for building reliable predictive models. The course places strong emphasis on methodological rigour: participants will learn how to move from raw, incomplete, noisy, and sometimes corrupted datasets to validated models, with careful attention to model selection, hyperparameter tuning, and error estimation.

A central component of the programme is the hands-on MATLAB implementation sessions. Participants will translate theory into practice by developing working code for regression, classification, clustering, and model evaluation workflows. The workshops are designed to guide students through the full pipeline, data preprocessing, feature engineering, model training, validation, and performance assessment, while highlighting reproducibility, numerical robustness, and engineering interpretability.

To ensure practical relevance, the course is grounded in real engineering case studies. Example applications include fuel consumption optimisation in propulsion systems, and performance monitoring of complex energy systems. These case studies help bridge the gap between algorithmic concepts and operational engineering practice, demonstrating how data-driven models can complement and, when properly validated, sometimes outperform traditional modelling approaches.

All course material will be made available to participants for continued reference after the summer school.

• Describe several models for supervised and unsupervised inference from data. Critically evaluate statistical analysis. Critically assess the fit of statistical models.  
• Assess the strengths and weaknesses of each of these models and interpret the mathematical equations from linear algebra, statistics, and probability theory used in the learning models. 
• Implement efficient learning algorithms in the MATLAB language, applied to engineering problems. 
• Design test procedures to evaluate the model hyperparameters (model selection) and its error (error estimation). Develop an appropriate experimental research design for an engineering case study considering practical limitations. 

To ensure that students grasp theoretical concepts and excel in their practical application, the course’s learning objectives will be assessed through a comprehensive coursework project. 

Objective: The coursework is designed to assess students’ understanding of theoretical AI principles and their ability to develop code that addresses tangible engineering challenges. It offers a platform for students to integrate and apply their knowledge in a practical, real-world context. 

Introduction and Discussion: The coursework will be introduced and thoroughly discussed on the last day of the course, which is scheduled for 28 August 2026. This session will provide students with a clear understanding of the coursework requirements, the scope of the problems to be addressed, and the expectations regarding coding and analytical skills. 

Duration and Submission: Upon introduction, students will have three weeks to complete and submit their coursework. This timeframe allows students to engage with the problem thoroughly, apply their skills effectively, and develop a well-considered solution. 

Components of Submission: The submission will consist of two primary components: 1) a detailed report where the students articulate the problem-solving approach, the application of AI methods, and the analysis of the results; 2) a Developed code well-documented, highlighting the application of AI techniques learned during the course. 

Assessment and Grading: Both the report and the code will be critically assessed to gauge the student’s proficiency in applying AI methods to solve engineering problems. The grades reflect students’ ability to integrate theoretical knowledge with practical skills.