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OXFORD ML SUMMER SCHOOL, 2020

WITH A SPECIAL FOCUS ON HEALTHCARE & (BIO)MEDICINE

OxML.2020 aims to provide the participants with best-in-class training on a broad range of advanced topics and developments in machine learning (ML) — including, deep learning. While the school covers the key topics in domains such as Bayesian ML, Computer Vision, NLP and reinforcement learning (as well as areas such as Causal ML, Topological ML, and Transfer Learning that the field is showing a growing interest in), there will be a special focus on medicine (the UN’s sustainable development goal #3), through a range of applied talks at the interface of machine learning and healthcare/biomedical sciences (e.g., medical imaging, genetics, drug discovery, and learning from multi-modal medical data).

 

Healthcare can be one of the biggest beneficiaries of AI. Today, we spend 10 trillion dollars on healthcare, globally (that is 10% of global GDP). Despite such a huge spend, according to a report by Geneva Association, there is still a healthcare protection gap of 3.6 trillion dollars outside the US and EU; that is the out-of-pocket spending on health that entails a high risk of financial hardship and even impoverishment. Therefore, "Good health and well-being" is one of the important goals in the UN's Global Goals and the pledge to leave no one behind.

 

Participants (i.e., graduate students, post-docs and professionals) will learn directly from world-renowned researchers and lecturers in ML (and biomedicine) in Oxford’s unique academic environment. It will be an excellent opportunity to learn about the present and future of ML research and the role it will play in healthcare and SDG.

 

Oxford ML Summer School, 2020 

17-22 August 2020

Oxford, UK

PARTNERS

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IMPORTANT DATES

January 2020

Applications Open

April 2020

Applications Close

May 2020

Acceptance Notification

August 2020

Oxford ML Summer School

 

SCHEDULE

A provisional program for the school is shared here; this will be updated in the coming weeks (see the timeline for more information). To register your interest for OxML.2020, please visit the school's website.