Image: Attendees come together at the University of Bath for the first Bath Machine Learning Summer School in 2026.
Researchers from across the UK and Europe came together at the University of Bath for the first Bath Machine Learning Summer School, a three-day programme designed to develop machine learning knowledge, share ideas and build connections across the research community.
Organised by the Centre for Artificial Intelligence and led by Simon Prince from the University of Bath, the Summer School took place from 5–7 August 2026 and combined tutorial-style teaching with opportunities for discussion and networking.
The programme explored both core and emerging areas of machine learning, ranging from ODEs and SDEs in machine learning and probabilistic and Bayesian methods to self-supervised learning, deep generative models and world models.
Simon Prince opened the teaching programme with sessions on ODEs and SDEs in machine learning. The Summer School also brought together Neill Campbell from University College London, Ivor Simpson from the University of Sussex, Oisin Mac Aodha from the University of Edinburgh, Gabriel Brostow from University College London and Deblina Bhattacharjee from the University of Bath.
The event attracted 109 registrations from 51 institutions, with participants predominantly comprising PhD and doctoral researchers, postgraduate students, postdoctoral researchers and research staff. The Summer School attracted exceptionally high interest, with applications closing early after demand exceeded the available places.
Feedback from participants was very positive, particularly around the quality and breadth of the teaching, the combination of theoretical and applied machine learning and the welcoming environment.
One participant described it as the “Best ML course I’ve done,” while another commented: “Thank you for a great summer school. I learnt a lot!”
The contribution of the speakers was also a highlight. One attendee commented that “the topics were selected very well and the quality of the lectures was very high,” adding that the speakers’ willingness to answer questions was particularly appreciated.
Others praised the “great atmosphere and discussion,” the broad range of talks and the opportunity to explore topics outside their immediate areas of research.
The Summer School also provided an opportunity to bring together researchers from different institutions and research backgrounds, creating space for conversations and new connections alongside the formal teaching programme.
Feedback has already provided plenty of ideas for developing the Summer School further. Participants expressed particular interest in more hands-on coding and practical sessions, as well as poster sessions and additional opportunities for socialising and networking.
Following the success and strong demand for the first Summer School, there are hopes that the Bath Machine Learning Summer School will become an annual event, continuing to bring together early-career researchers to learn about developments in machine learning, exchange ideas and build connections across the research community.
As one participant summed it up: “Great experience! Keep up! :)”
Watch the video recap to see highlights from the Bath Machine Learning Summer School 2026:
Image: Simon Prince delivering a session at the Bath Machine Learning Summer School 2026.
Image: A session in full swing at the inaugural Bath Machine Learning Summer School 2026 with Deblina Bhattacharjee and Simon Prince.
Image: Speakers at the inaugural Bath Machine Learning Summer School 2026. Left-Right: Ivor Simpson, Simon Prince, Deblina Bhattacharjee, Neill Campbell, Oisin Mac Aodha, Gabriel Brostow
