51³Ô¹ÏÍø

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 theÌýcentral roleÌýoptimisation 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.Ìý

ÌýThisÌýOptimisation Accelerator is an intensive, hands-on course taught by leading optimisation experts from 51³Ô¹ÏÍø 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.Ìý

No prior knowledge of optimisation isÌýrequired.Ìý

The course provides a comprehensive introduction to the formulation and solution of optimisation problems, covering:Ìý

• Linear Programming (LP)ÌýÌý
• Nonlinear Programming (NLP)ÌýÌý
• Mixed-Integer Programming (MIP)ÌýÌý
• Global Optimisation (GO)ÌýÌý
• Optimisation under UncertaintyÌýÌý
• Multi-Objective OptimisationÌý
• Bayesian OptimisationÌýÌý
• Neural Network Training and optimisation methods in machine learningÌýÌý

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.Ìý

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.Ìý

WhatÌýYou’llÌýLearnÌý

By the end of the course, participants will be able to:Ìý

• Understand the foundations of optimisation modellingÌýÌý
• Formulate optimisation problems from practical applicationsÌýÌý
• Distinguish between linear, nonlinear, integer, and global optimisation approachesÌýÌý
• Apply optimisation techniques using modern software toolsÌýÌý
• Understand optimisation under uncertainty and multi-objective trade-offsÌýÌý
• Explore Bayesian optimisation and optimisation methods for neural network trainingÌýÌý
• Gain practical experience through hands-on workshops and real examplesÌýÌý
• Bring and discuss their own optimisation problems with instructors and peersÌýÌý

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Registration Fee:

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Cancellations

Substitutions may be made at any time, whilst a valid place is held. The organiser cannot accept liability for costs incurred in the event of a course having to be cancelled as a result of circumstances beyond its reasonable control.