Connectionists: Oxford Machine Learning Summer School (OxML 2021) -- Call for Application

[Reza Khorshidi]

We are excited to announce that the application for Oxford Machine Learning Summer School (OxML 2021) is now open.
Location: Virtual/Online
Dates: August 9-20, 2021 (two weeks)
Application deadline: April 30, 2021
Website: www.oxfordml.school <https://www.oxfordml.school/>

About OxML
OxML schools have a special focus on ML and SDG <https://sustainabledevelopment.un.org/>; in addition to theoretical ML lectures, there will also be lectures on the applications of ML in SDG topic areas.
OxML 2021 is organised by AI for Global Goals, and in partnership with The University of Oxford’s Deep Medicine Program, and CIFAR <https://cifar.ca/>.
You can find out more about last year’s event (OxML 2020) — including the previous speakers and agenda — here <https://www.oxfordml.school/oxml2020>. Last year, we hosted participants from 70+ countries and connected them to our world-renown speakers in ML (and in ML's applications in medicine, or SDG3).
Due to the pandemic, and in order to prioritise our speakers’ and participants’ health and safety, OxML 2021 will take place August 9-20, 2021 and will be an online event.


OxML 2021 will cover more topics in both theoretical and applied ML
The more theoretical lectures on modern ML/DL topics will cover:
Statistical/probabilistic ML (e.g., Bayesian ML, Gaussian processes, ...)
Representation learning, computer vision, and NLP (e.g., neural sequence models, knowledge graphs, ...)
Causal ML (e.g., probabilistic graphical models, causal deep learning, ...)
Graph neural networks and Geometric DL
Federated learning, transfer learning, knowledge distillation, and more

The applied lectures (on ML and SDG) will cover:
ML and Medicine (e.g., imaging, genomics, electronic health records (EHR), drug discovery, …) [~2 days]
ML and Social Good (e.g., climate action, emerging risks, sustainable cities, …) [~1.5 days]


Speakers
The school’s world-renowned speakers are from top ML research groups (e.g., Oxford, Cambridge, CIFAR, Amazon, DeepMind, Microsoft Research, and more). The speakers’ bios and more details on their talks will be announced in the coming weeks (you can follow the updates via the school’s website <https://www.oxfordml.school/> and Twitter <https://twitter.com/GlobalGoalsAI> handle).


Target audience 
Everyone is welcome to apply to OxML 2021 regardless of their origin, nationality, and country of residence.
Our primary target audience are (1) PhD students with good technical background whose research topics are related to ML, plus (2) researchers and engineers in both academia and industry with similar/advanced levels of technical knowledge.
All applicants are subject to a selection process; we aim to select strongly motivated participants, who are interested in broadening their knowledge of the advanced topics in the field of ML/DL and their applications.


Application 
The application deadline is April 30, 2021; those interested can apply through the application page <https://docs.google.com/forms/d/e/1FAIpQLSfVriKTBkFLOYG_RFRcLruoUtN8Y0Dmaf-veP9rmJ55a2Bumg/viewform?usp=sf_link> or via the School's website. 
The application portal may close earlier than the deadline if the number of applications exceeds our capacity to review (last year we had more than 10x more applicants than we planned to accept).
There is no application fee.
For the accepted applicants, there will be a registration fee (please refer to the application form or FAQ <https://www.oxfordml.school/faq> page for more details).
Participants will have access to all lectures, plus the event’s community portal (e.g., Q&A and chat environment for participants and speakers/TAs, ML programming workshops, recordings of the lectures [for a limited time], the unconference track, networking, and more). 


Contacting Us 
For any queries, you can contact us using this email address: contact at oxfordml.school <mailto:contact at oxfordml.school>
Of course, more information (and possible the answers to some of the questions) can be found on the FAQ section of the website.

Best,
—
Reza Khorshidi, D.Phil. (Oxon)
Deep Medicine Program, Oxford Martin School
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