<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div class=""><span class="" style="font-size: 14px;">Dates: August 7-14, 2022 (St Catherine's College, Oxford + Virtual)</span></div><div class=""><span class="" style="font-size: 14px;">Application deadline: 15 April, 2022 </span></div><div class=""><span class="" style="font-size: 14px;">For more info, please visit the school’s website: <a href="https://www.oxfordml.school/" rev="en_rl_none" class="">www.oxfordml.school</a></span></div><div class=""><span class="" style="font-size: 14px;">Link to the application form: <a href="https://forms.gle/EqvC3qxKmoGKJgGs5" rev="en_rl_none" class="">https://forms.gle/EqvC3qxKmoGKJgGs5</a></span></div><div class=""><br class=""></div><div class=""><br class=""></div><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><b class="" style="font-size: 14px;">About OxML 2022</b></div><ul class=""><li class=""><span class="" style="font-size: 14px;">OxML is organised by AI for Global Goals, in partnership with <a href="https://cifar.ca/" rev="en_rl_none" class=""><span class="" style="color: rgb(0, 0, 0);">CIFAR</span></a> and The University of Oxford’s <a href="http://deepmedicine.medsci.ox.ac.uk/" rev="en_rl_none" class=""><span class="" style="color: rgb(0, 0, 0);">Deep Medicine</span></a> Program.</span></li><li class=""><span class="" style="font-size: 14px;">OxML schools have a special focus on ML and <a href="https://sustainabledevelopment.un.org/" rev="en_rl_none" class=""><span class="" style="color: rgb(0, 0, 0);">SDG</span></a>s. That is, in addition to theoretical ML lectures, there will be lectures on the application of ML in various SDGs areas.</span></li><li class=""><span class="" style="font-size: 14px;">OxML 2022 will have two separate 4-day schools: (1) ML x Health, and (2) ML x Finance. </span></li><li class=""><span class="" style="font-size: 14px;">Furthermore, based on the success of last year's program, and in order to provide all participants with the necessary background -- particularly for those who are new to the theory and fundamentals of modern ML -- the program will also have an online ML Fundamentals module (June 27-29), which will be open to both schools' accepted participants.</span></li><li class=""><span class="" style="font-size: 14px;">The schools will take place in <b class="">St Catherine's College, Oxford (UK)</b>. There will also be a virtual option for those who cannot (or prefer not to) travel to the UK.</span></li><li class=""><span class="" style="font-size: 14px;">During each school, in addition to applied and theoretical lectures (taking place in the main hall, with ~250 seats), there will be multiple workshops and sessions on Advanced ML topics, ML Ops, ML Products, and ML Career (taking place in the 4x smaller halls, that have ~50-100 seats).</span></li><li class=""><span class="" style="font-size: 14px;">We aim to host ~200 participants in person (plus 100-200 virtually) in each school. Note that, while our current plan is to have a hybrid format, in the worst case COVID scenarios, we have (and are ready to execute) a plan B to go fully virtual. </span></li><li class=""><span class="" style="font-size: 14px;">You can find out more about the previous schools — including the previous speakers — <a href="https://www.oxfordml.school/2021" rev="en_rl_none" class=""><span class="" style="color: rgb(0, 0, 0);">here</span></a>.</span></li></ul><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><span class="" style="font-size: 14px;">The schools' theoretical tutorials on modern ML (including DL) will cover topics such as:</span></div><ul class=""><li class=""><span class="" style="font-size: 14px;">Neural networks, deep learning / representation learning (with, with little, or without supervision), ...</span></li><li class=""><span class="" style="font-size: 14px;">Statistical/probabilistic ML (e.g., Bayesian ML, Gaussian processes, variational inference, Bayesian neural networks)</span></li><li class=""><span class="" style="font-size: 14px;">Neural sequence models (e.g., Transformers)</span></li><li class=""><span class="" style="font-size: 14px;">NLP, computer vision, and multi-modal representation learning</span></li><li class=""><span class="" style="font-size: 14px;">Reinforcement learning</span></li><li class=""><span class="" style="font-size: 14px;">Causal ML</span></li><li class=""><span class="" style="font-size: 14px;">Graph neural nets and Geometric DL</span></li><li class=""><span class="" style="font-size: 14px;">Learning theory</span></li><li class=""><span class="" style="font-size: 14px;">Optimisation</span></li><li class=""><span class="" style="font-size: 14px;">...</span></li></ul><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><span class="" style="font-size: 14px;">On the applied side, the school will cover topics such as:</span></div><ul class=""><li class=""><span class="" style="font-size: 14px;">ML x Health: The applications of ML in imaging, genomics, electric health records (EHR), drug discovery, ...</span></li><li class=""><span class="" style="font-size: 14px;">ML x Finance: The applications of ML in sustainable investing (e.g., ESG), insurance and emerging risks, banking , hedging and options trading, ...</span></li></ul><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><b class="" style="font-size: 14px;"><br class=""></b></div><div class=""><b class="" style="font-size: 14px;"><br class=""></b></div><div class=""><b class="" style="font-size: 14px;">Speakers</b></div><div class=""><span class="" style="font-size: 14px;">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 see our previous speakers <a href="https://www.oxfordml.school/2021" rev="en_rl_none" class="">here</a>, and follow the updates via the <a href="https://www.oxfordml.school" rev="en_rl_none" class="">school’s website</a>, or <a href="https://twitter.com/GlobalGoalsAI" rev="en_rl_none" class="">Twitter</a> and <a href="https://www.linkedin.com/company/ai-for-global-goals/" rev="en_rl_none" class="">LinkedIn</a> accounts). </span></div><div class=""><span class="" style="font-size: 14px;"><br class=""></span></div><div class=""><span class="" style="font-size: 14px;"><br class=""></span></div><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><b class="" style="font-size: 14px;">Target audience </b></div><ul class=""><li class=""><span class="" style="font-size: 14px;">Everyone is welcome to apply to OxML 2022 regardless of their origin, nationality, and country of residence. </span></li><li class=""><span class="" style="font-size: 14px;">Our target audience are (1) PhD students with a 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. </span></li><li class=""><span class="" style="font-size: 14px;">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.</span></li></ul><div class=""><span class="" style="font-size: 14px;"><br class=""></span></div><div class=""><span class="" style="font-size: 14px;"><br class=""></span></div><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><b class="" style="font-size: 14px;">Application</b></div><div class=""><span class="" style="font-size: 14px;">You can find the application for for the school <a href="https://forms.gle/EqvC3qxKmoGKJgGs5" rev="en_rl_none" class="">here</a>.</span></div><div class=""><span class="" style="font-size: 14px;">Given the overwhelming number of applications we received in previous years, the application portal may close earlier than the deadline if the number of applications exceeds our capacity to review.</span></div><div class=""><span class="" style="font-size: 14px;">For any queries, you can contact us using this email address: <a href="mailto:contact@oxfordml.school" rev="en_rl_none" class="">contact@oxfordml.school</a></span></div><div class=""><br class=""></div><div class=""><br class=""></div><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><span data-markholder="true" class="" style="font-size: 14px;"></span></div><div class=""><span class="" style="font-size: 14px;">Best,</span></div><div class=""><span class="" style="font-size: 14px;">—</span></div><div class=""><span class="" style="font-size: 14px;">Reza Khorshidi, D.Phil. (Oxon)</span></div><div class=""><span class="" style="font-size: 14px;">Deep Medicine Program, The University of Oxford</span></div></body></html>