Optimisation is the workhorse behind modern machine learning and AI. From training neural networks and tuning hyperparameters to resource allocation and decision-making systems, optimisation algorithms are at the core of how intelligent systems learn and improve. This course highlights the central role optimisation plays across engineering, data science, and machine learning, giving participants both the theoretical foundations and practical tools needed to apply optimisation methods in real-world applications. This Optimisation Accelerator is an intensive, hands-on course taught by leading optimisation experts from Imperial College London and University College London. The programme is designed for industry practitioners seeking practical optimisation skills with immediate real-world relevance, PhD students and postdoctoral researchers. Participants who successfully complete the course will receive a certificate of completion. No prior knowledge of optimisation is required. The course provides a comprehensive introduction to the formulation and solution of optimisation problems, covering: Linear Programming (LP) Nonlinear Programming (NLP) Mixed-Integer Programming (MIP) Global Optimisation (GO) Optimisation under Uncertainty Multi-Objective Optimisation Bayesian Optimisation Neural Network Training and optimisation methods in machine learning Participants will learn how to translate real-world engineering and data-driven challenges into optimisation models and solve them using modern software tools through guided hands-on sessions. While the course primarily focuses on local optimisation methods, it also introduces advanced topics such as global optimisation, uncertainty-aware optimisation, and emerging optimisation techniques used in machine learning and AI workflows. What You’ll Learn By the end of the course, participants will be able to: Understand the foundations of optimisation modelling Formulate optimisation problems from practical applications Distinguish

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