BEGIN:VCALENDAR PRODID:-//eluceo/ical//2.0/EN VERSION:2.0 CALSCALE:GREGORIAN BEGIN:VEVENT UID:682d091903ff283a6736da641bc1bb5a DTSTAMP:20260606T101247Z SUMMARY:Optimisation Accelerator: Improving Decisions through Optimisation DESCRIPTION:\n\nOptimisation is the workhorse behind modern machine learnin g and AI. From training neural networks and tuning hyperparameters to reso urce allocation and decision-making systems\, optimisation algorithms are at the core of how intelligent systems learn and improve. This course high lights the central role optimisation plays across engineering\, data sci ence\, and machine learning\, giving participants both the theoretical fou ndations and practical tools needed to apply optimisation methods in real- world applications.  \nThis Optimisation Accelerator is an intensive\, hands-on course taught by leading optimisation experts from Imperial Col lege London and University College London. The programme is designed for  industry practitioners seeking practical optimisation skills with immediat e real-world relevance\, PhD students and postdoctoral researchers.  Participants who successfully complete the course will receive a certific ate of completion. \nNo prior knowledge of optimisation is required. \n The course provides a comprehensive introduction to the formulation and so lution of optimisation problems\, covering: \n\nLinear Programming (LP)   \nNonlinear Programming (NLP)  \nMixed-Integer Programming (MIP)  \n Global Optimisation (GO)  \nOptimisation under Uncertainty  \nMulti-Ob jective Optimisation \nBayesian Optimisation  \nNeural Network Training and optimisation methods in machine learning  \n\nParticipants will lea rn how to translate real-world engineering and data-driven challenges into optimisation models and solve them using modern software tools through gu ided hands-on sessions. \nWhile the course primarily focuses on local opt imisation methods\, it also introduces advanced topics such as global opti misation\, uncertainty-aware optimisation\, and emerging optimisation tech niques used in machine learning and AI workflows. \nWhat You’ll Learn  \nBy the end of the course\, participants will be able to: \n\nUndersta nd the foundations of optimisation modelling  \nFormulate optimisation p roblems from practical applications  \nDistinguish between linear\, nonl inear\, integer\, and global optimisation approaches  \nApply optimisati on techniques using modern software tools  \nUnderstand optimisation und er uncertainty and multi-objective trade-offs  \nExplore Bayesian optimi sation and optimisation methods for neural network training  \nGain prac tical experience through hands-on workshops and real examples  \nBring a nd discuss their own optimisation problems with instructors and peers  \ n\nCourse Format \nThe programme combines: \n\nShort lectures introducin g key concepts  \nInteractive software demonstrations  \nGuided hands- on optimisation sessions  \nIndustry-relevant examples and case studies   \nOpportunities for discussion and networking  \n\nA welcome session with food and drinks and dedicated “Bring Your Own Optimisation Problem ” activities encourage collaboration across academia and industry. \nPr ogramme Overview \nDay 1 – Foundations of Optimisation (12.30 start)\n \nIntroduction / Why Optimise?  \nPrinciples of Nonlinear Programming (N LP)  \nIntro to Software and NLP Hands-on Session  \nWelcome with Food and Drinks  \n\nDay 2 – Linear and Integer Optimisation \n\nPrincipl es of Linear Programming (LP)  \nLP Hands-on Session  \nPrinciples of Mixed-Integer Programming (MIP)  \nMIP Hands-on Session  \nPrinciples of Global Optimisation (GO)  \nGO Hands-on Session  \n\nDay 3 – Opti misation under Uncertainty and Multiple Objectives \n\nPrinciples of Opti misation under Uncertainty  \nHands-on Session: Optimisation under Uncer tainty  \nPrinciples of Multi-Objective Optimisation  \nHands-on Sessi on: Multi-Objective Optimisation  \nBring Your Own Optimisation Problem   \n\nDay 4 – Modern Optimisation for AI and Machine Learning \n\nPri nciples of Bayesian Optimisation  \nHands-on Session: Bayesian Optimisat ion  \nPrinciples of Neural Network Training  \nHands-on Session: Neur al Network Training  \n\nWho Should Attend? \nThis course is ideal for:  \n\nPhD students and postdoctoral researchers  \nEngineers and technic al specialists  \nData scientists and data analysts  \nR&D professio nals  \nIndustry practitioners interested in optimisation-driven decisio n making  \n\nThe course is specifically designed for technically minded participants seeking practical optimisation skills rather than senior-lev el strategy content. \nParticipants will leave with both a strong concept ual foundation and practical experience applying optimisation techniques t o real-world problems. \n\n\n\n\nRegistration information\nPractical Info rmation\n\n\n\n\nRegistration Fee:\n\n\n\n\nIndustry rate\n £ 1700\n (f rom 1 June 2026)\n\n\nEarly bird Industry rate\n£ 1400\n (until 31 May 2 026)\n\n\nStart up/SME rate\n £ 975\n(from 1 June 2026)\n\n\nEarly bird Start up/ SME rate\n£ 750\n(until 31 May 2026)\n\n\nAcademic rate\n£   585\n(from 1 June 2026)\n\n\nEarly bird academic rate\n £   450\n(un til 31 May 2026)\n\n\n\n\n \nCancellations\nWritten cancellations receive d by 17 August 2026 are eligible for a partial refund (80%). No refunds wi ll be given after 17 August 2026.\nSubstitutions may be made at any time\, whilst a valid place is held. The organiser cannot accept liability for c osts incurred in the event of a course having to be cancelled as a result of circumstances beyond its reasonable control.\n\n\n\n URL:https://www.imperial.ac.uk/events/209871/optimisation-accelerator-impro ving-decisions-through-optimisation/ DTSTART;TZID=Europe/London:20260907T123000 DTEND;TZID=Europe/London:20260910T170000 LOCATION:TBC\, Roderic Hill Building\, South Kensington Campus\, Imperial C ollege London\, London\, SW7 2AZ\, United Kingdom END:VEVENT BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT DTSTART:20260907T123000 TZNAME:BST TZOFFSETTO:+0100 TZOFFSETFROM:+0100 END:DAYLIGHT END:VTIMEZONE END:VCALENDAR