***Due to the ongoing covid-19 pandemic we have taken the decision to postpone the workshop by 1 year. The workshop will now be held in two parts. The first part is online only and will take place 26th-30th July 2021. The second part is in-person and will be held in January 2022 at the University of Bath.***
As we enter the age of data we have seen the birth of machine learning methods to a diverse range of applications, including computer vision, classification/clustering, regression, data mining and prediction. Machine learning has been remarkably successful in applications but our theoretical understanding of many machine learning algorithms is still missing. This has led to an increasing appetite for the mathematical analysis of machine learning algorithms. Particularly exciting is the potential for methods from applied mathematics, probability theory, and statistics to contribute to machine learning theory.
The aim of the workshop is to bring together researchers that apply mathematical methodology to machine learning. We particularly want to emphasise how mathematical theory can inform applications and vice versa.
This workshop is the first of two workshops on this topic. The second will be an in-person workshop to be held at the University of Bath, in December 2021. In this first workshop, invited speakers are encouraged to present open problems and explore interesting directions for potential research as part of their talk. The schedule allows participants time to initiate conversations and collaborations that can be developed at the winter workshop.
The two workshops in this series follow on from the LMS-Bath Symposium on the Mathematics of Machine Learning held 3-7 August 2020.
Please complete the registration form here to join the summer workshop.
The schedule for the summer workshop is below.
Titles and abstracts are below.
The slides from the workshop opening are below.
The workshop is funded by the International Centre for Mathematical Sciences (ICMS) and supported by NoMADS (which in turn received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 777826). We are grateful for the hospitality of the University of Bath.