Original Thinking Webinar
|Starts:||16:30 6 Oct 2021|
|Ends:||17:30 6 Oct 2021|
|What is it:||Talk|
|Organiser:||Alliance Manchester Business School|
|Who is it for:||University staff, Adults, Alumni, Current University students, General public|
We all face and make decisions on an ongoing basis, whether at work or in our private lives. The vast majority of these decisions involve trade-offs between multiple criteria, be it healthiness versus taste in our choice of breakfast cereal, cost versus energy efficiency in our choice of a new household utility, or risk versus expected return in our selection of a financial portfolio. Typically, there is considerable conflict between these criteria and, in the presence of such conflict, a single optimal solution may not exist. Taking a sound decision will then require the exploration of a set of alternative trade-offs, and the incorporation of additional preference information.
The same types of trade-offs exist in machine learning applications, where our models frequently have to strike a compromise between a variety of conflicting criteria. In this presentation, I will discuss the various origins of these criteria in a machine learning context. Using a number of examples from my own research, I will then highlight how multicriterion optimisation can support us in exploring a range of alternative trade-off solutions for machine learning problems, supporting the analyst in identifying their preferred model.
Role: Professor in Data Sciences
Organisation: Alliance MBS
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