Organised by AI for Global Goals
Oxford Machine Learning Summer School
20–21 July &
9–20 August
Virtual
The second Oxford machine learning summer school (OxML 2021), aims to provide its participants with best-in-class training on a broad range of advanced topics and developments in machine learning (ML) and deep learning (DL). The school will cover some of the most important topics in ML/DL that the field is showing a growing interest in (e.g., Bayesian ML, representation learning, computer vision, natural language processing (NLP), reinforcement learning, causal ML, and transfer learning).
This year, in addition to SDG #3 (healthcare/medicine), OxML 2021 will have additional focus areas like AI for good (e.g., climate action, emerging risks, sustainable cities, and more). This will enable us to cover a broader range of global goals from a machine learning perspective.
The field of Machine Learning (including Deep Learning) has seen tremendous growth, in the past decade. Accumulation of large datasets, availability of affordable computing power at scale, and new developments in intelligent algorithms are known to be the key drivers of this growth in the field. The number of ML papers on arXiv has grown faster than Moore's law, surpassing 100 papers per day in 2018, and keeps growing. While such developments have simultaneously led to both optimistic and pessimistic views on AI and the future of society/humanity, one thing everyone agrees on is that certain industries and domains have benefited from AI more than others.
The Global Goals (i.e., the 17 goals officially known as the UN Sustainable Development Goals, or SDGs) can be among the biggest beneficiaries of AI. According to a report by PwC, by 2030, the use of AI for environmental applications could contribute up to $5.2tn to the global economy; it could also create 38.2million net new jobs globally. Adding the impact to overlapping industries such as healthcare, finance, education and more, the size of this impact can be even bigger.
OxML aims to bring some of the best talents in machine learning together with the primary goal of providing them with world-class training in ML/DL, with a special focus on ML/DL used for Healthcare and Social Good (e.g.insurance and climate action). Our main objective is to raise awareness about AI+SDG, and train and inspire more scientists and engineers in ML to pursue research towards the SDG’s pledge to leave no one behind.
MACHINE LEARNING & SDGs
HEALTHCARE & SOCIAL GOOD
IMPORTANT DATES
17 February 2021
Applications Open
30 April 2021
Applications Close
7 June 2021
Acceptance Notification
20–21 July: ML Fundamentals
9–20 August: OxML 2021
SPEAKERS
REPRESENTATION LEARNING & STATISTICAL ML
NATURAL LANGUAGE PROCESSING (NLP)
ML IN HEALTHCARE
AI FOR GOOD
Adam Wierman
Thomas Dietterich
David Rolnick
Sohee Park
Naren Ramakrishnan
Caltech
Oregon State University
McGill University, Mila
Virginia Tech.
Ping An
Deniz Gunduz
Imperial College London
Daniele Magazzeni
J.P. Morgan,
Kings College London
Renyuan Xu
Oxford University
Jacob Abernethy
Georgia Institute of Technology
ML FUNDAMENTALS
SPEAKERS
REPRESENTATION LEARNING & STATISTICAL ML
NATURAL LANGUAGE PROCESSING (NLP)
ML IN HEALTHCARE
AI FOR GOOD
Thomas Dietterich
David Rolnick
Adam Wierman
Naren Ramakrishnan
Caltech
McGill University, Mila
Oregon State University
Virginia Tech.
ML FUNDAMENTALS
PROGRAM
Rep. Learning & Statistical ML
ML in Healthcare
Natural Language Processing
AI for Good
PROGRAM COMMITEE
SPONSORS
DIAMOND SPONSORS
GOLD SPONSORS
PARTNERS
OxML schools are organised by AI for Global Goals and in partnership with CIFAR and the University of Oxford's Deep Medicine Program
CONTACT US
If you are interested in attending our schools, discuss sponsorship/partnership opportunities, or simply stay updated about our progress, please send us an e-mail, or subscribe to our mailing list, below.