Workshop theme
This Open for Business event is part of an INI programme on Representing, calibrating & leveraging prediction uncertainty from statistics to machine learning. It aims to provide a meeting ground to facilitate interactions and exchanges between representatives of academia, research and industry, relevant to the theme, with the objective of identifying points of mutual interest and possible co-activity.

Background 
Machine Learning is transforming corporate and scientific activities. Its power resides in complex models, the data that fuels them, and the algorithms that adapt the models to the data. Uncertainty is present at many levels in the resulting pipeline, for example, in model predictions, in model hyperparameter estimates, in the underlying data, and in the structure of generative models. Methods for representing and calibrating uncertainties in machine learning models are an area of active research, especially when the objects of interest are complex, such as text, images, conception specifications, etc. In some situations, uncertainties can be detrimental, especially if they are not well understood or quantified. But the presence of uncertainty is also an opportunity, as uncertainty can be leveraged to explore parameter spaces resulting in improved prediction, leading to breakthroughs and discoveries, and the accurate quantification of uncertainty can aid decision making and increase user confidence.

Aims
In this event, we will hear from machine learning researchers from industry and academia, to discuss the challenges and opportunities posed for corporate, scientific, and societal activities with regard uncertainty in machine learning models. This Newton Gateway Open for Business Event will take place within the INI Programme Representing, calibrating & leveraging prediction uncertainty from statistics to machine learning.

Event webpage