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 between linear, nonlinear, integer, and global optimisation approaches
- Apply optimisation techniques using modern software tools
- Understand optimisation under uncertainty and multi-objective trade-offs
- Explore Bayesian optimisation and optimisation methods for neural network training
- Gain practical experience through hands-on workshops and real examples
- Bring and discuss their own optimisation problems with instructors and peers
Course Format
The programme combines:
- Short lectures introducing key concepts
- Interactive software demonstrations
- Guided hands-on optimisation sessions
- Industry-relevant examples and case studies
- Opportunities for discussion and networking
A welcome session with food and drinks and dedicated “Bring Your Own Optimisation Problem” activities encourage collaboration across academia and industry.
Programme Overview
Day 1 – Foundations of Optimisation (12.30 start)
- Introduction / Why Optimise?
- Principles of Nonlinear Programming (NLP)
- Intro to Software and NLP Hands-on Session
- Welcome with Food and Drinks
Day 2 – Linear and Integer Optimisation
- Principles of Linear Programming (LP)
- LP Hands-on Session
- Principles of Mixed-Integer Programming (MIP)
- MIP Hands-on Session
- Principles of Global Optimisation (GO)
- GO Hands-on Session
Day 3 – Optimisation under Uncertainty and Multiple Objectives
- Principles of Optimisation under Uncertainty
- Hands-on Session: Optimisation under Uncertainty
- Principles of Multi-Objective Optimisation
- Hands-on Session: Multi-Objective Optimisation
- Bring Your Own Optimisation Problem
Day 4 – Modern Optimisation for AI and Machine Learning
- Principles of Bayesian Optimisation
- Hands-on Session: Bayesian Optimisation
- Principles of Neural Network Training
- Hands-on Session: Neural Network Training
Who Should Attend?
This course is ideal for:
- PhD students and postdoctoral researchers
- Engineers and technical specialists
- Data scientists and data analysts
- R&D professionals
- Industry practitioners interested in optimisation-driven decision making
The course is specifically designed for technically minded participants seeking practical optimisation skills rather than senior-level strategy content.
Participants will leave with both a strong conceptual foundation and practical experience applying optimisation techniques to real-world problems.
Registration Fee:
| Industry rate | £ 1700 | (from 1 June 2026) |
| Early bird Industry rate | £ 1400 | (until 31 May 2026) |
| Start up/SME rate | £ 975 | (from 1 June 2026) |
| Early bird Start up/ SME rate | £ 750 | (until 31 May 2026) |
| Academic rate | £ 585 | (from 1 June 2026) |
| Early bird academic rate | £ 450 | (until 31 May 2026) |
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.
Venue: The Sargent Centre for Process Systems Engineering
Imperial College London
Roderic Hill Building
South Kensington Campus
London SW7 2BB
For the campus website, use this link.
Closest Underground Stations are South Kensington or Gloucester Road.
