Location:  FULLY ONLINE

The Cambridge Centre for AI in Medicine is delighted to host a Summer School dedicated to Machine Learning and Healthcare, designed for PhD students, academics, and industry professionals.
This Summer School will offer a more focused and advanced curriculum compared to other programs, emphasizing technical, cutting-edge, and reality-centric topics.

What makes this Summer School novel & different & unique?

• Dedicated to Machine Learning and Healthcare
• Not only machine learners, but also clinicians, epidemiologists, statisticians.
• Covering a wide range of advanced Machine Learning topics with use across the ML pipeline and diverse applications, including generative AI and large language models, Data-centric AI, causal inference, representation learning, time series, uncertainty estimation, responsible AI, and much more.
• Roundtables: industry presenting key challenges
• Create mini projects: work with the CCAIM open software

Secure your spot at our Summer School at an Early Bird rate until 04 April - afterwards, prices will rise to the normal rate. Spaces for participants are limited.

Location:  FULLY ONLINE

The Cambridge Centre for Artificial Intelligence in Medicine is pleased to offer a Summer School dedicated to AI in Healthcare. It is aimed at medical professionals, medical students and industry professionals. 

While the Summer School will not require deep prior knowledge, it will still be a fast-paced learning environment equipping you for the future of medicine.

What makes this summer school unique?

•    Elevate your expertise at an AI and ML summer school exclusively crafted for physicians, medical students, and industry professionals like yourself.
•    Gain insights from keynotes delivered by world-renowned experts.
•    Dive into hands-on mini projects, working with software and engineers from Cambridge and beyond.
•    Receive guidance from outstanding mentors.

Secure your spot at our Summer School at an Early Bird rate until 04 April - afterwards, prices will rise to the normal rate. Spaces for participants are limited.