Combinatorial Algebraic GeometryOrganisers: Johannes Hofscheier (Nottingham)
Diane Maclagan (Warwick)
Gregory G. Smith (Queen's)
This online summer school will be aimed at PhD students and postdocs in algebraic geometry. The core activity will be the three series of talks described below. There will also be lots of time for doing exercises! These will be complemented by accessible seminar talks and professional development activities.
PDE and RandomnessOrganisers: Professor Hendrik Weber
Dr Andris Gerasimovics
PDE and Randomness symposium, 1st - 10th of September 2021, University of Bath, UK26 - 30 07/2021
Analytic and Geometric Approaches to Machine LearningOrganisers: Dr Matthew Thorpe (The University of Manchester)
Dr Patricia Vitoria Carrera (Universitat Pompeu Fabra, Barcelona)
Dr Bamdad Hosseini (California Institute of Technology)
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 ...
LMS-INI- Bath Summer School on K-Theory & Representation TheoryOrganisers: Dr. Haluk Şengün, University of Sheffield
Prof. Roger Plymen, University of Manchester
Prof. Nigel Higson, PennState University
This is the summer school part of the LMS-INI-Bath Symposium on K-theory and Representation Theory (which will take place in July 2022). In this summer school, we will cover material that is foundational to the upcoming symposium, namely, representation theory of real and p-adic Lie groups, the theory of C*-algebras and operator K-theory, and fi...03 - 07 07/2020
Mathematics of Machine LearningOrganisers: Philip Aston, University of Surrey, UK
Matthias J. Ehrhardt, University of Bath, UK
Catherine Higham, University of Glasgow, UK
Clarice Poon, University of Bath, UK
Machine Learning (ML) can described as statistical and numerical methods which underpin modern algorithms for detecting patterns and inference. A common theme to many activities in ML today is that mathematical models based on sample data are used to train algorithms to work on real data. Applications include self-driven cars, fraud detection, a...