具体可见官网网址：
http://www.ellis.eng.cam.ac.uk/summer-school/
The Cambridge Ellis Unit Summer School on Probabilistic Machine Learning is from 15-19 July 2024 at the Department of Computer Science and Technology.
https://www.cl.cam.ac.uk/maps/
The
Cambridge Ellis Unit Summer School on Probabilistic Machine Learning is
a distinguished course offered to graduate students, researchers and
professionals, featuring engaging experts in their respective field
and/or world-recognized professionals speaking about advanced machine
learning concepts.
Uncertainty Quantification
Evaluation of Probablistic Models
Introduction to Diffusion Models
Application of Diffusion Models
Variational Inference and Stein Discrepancy
Probablistic Models in Computer Vision and Graphics
Machine Learning and the Physical World
The Summer School will be in person and held at the Department of Computer Science and Technology.
You can see travel information here.
This is an inperson event but we will record talks when we can and put on our Youtube channel.
All attendees will need to cover their own accommodation and travel costs.
Travel awards are available for attendees from under-represented backgrounds. Those selected to attend will then be given a chance to apply for the travel grant. Please email [email protected] for more information.
There are no fees to attend the Summer School.
Lunch and tea/coffee will be provided.
Those wishing to attend the Cambridge Ellis Machine Learning Summer School will need to complete this form:
https://forms.gle/RLWKLZJH9fQXBTPx6
You will also have to supply:
2.Letter of Reference: The writer should assess the qualities, characteristics, and capabilities of the person being recommended in terms of that individual’s abilities. The letter should address the applicant’s background and potential in Machine Learning, academic standing compared to other students, and how he or she would benefit from attending Cambridge Ellis Machine Learning Summer School. The referee should be a person that has some experience working together with the applicant. It can be for example a Ph.D. supervisor, a former employer or manager.
