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
17 February 2021
30 April 2021
31 May 2021
9-20 August 2021
OxML summer school
Area Chair - Statistical ML
Professor of Neuroimaging Statistics at Oxford University
Below is the provisional program of OxML 2021's main track; some minor changes are likely. The school will also have an unconference track, where participants will present their research and our selected volunteers will run ML programming workshops.
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.