OxML 2022
University of Oxford's St Catherine's College
7-14 August, 2022
ML Fundamentals
27-29 June, 2022
Virtual

Based on the success of previous years' 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 -- during this module, we aim to provide everyone with training in the following topics:
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Fundamentals of statistical / probabilistic ML
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Fundamentals of representation / deep learning
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Optimisation
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Mathematics of machine learning
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And more
ML Fundamentals Speakers
ML x HEALTH
7-10 August, 2022
Oxford St Catherine's College & Online

Building on the topics covered in ML fundamentals module, the Health module will continue and cover the following topics:
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Statistical / probabilistic ML (e.g., Bayesian ML, causal inference, approximate inference, modelling uncertainty, ...)
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Advanced topics in representation learning (e.g., learning with little or nor supervision, self-supervised learning, multi-modal representation learning, ...)
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Graph neural networks, and geometrical deep learning
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Computer vision
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Knowledge graphs
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Knowledge-aware ML
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Symbolic reasoning,
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Neuro-symbolic AI
Applied talks on ML in/for:
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EHR, imaging (e.g., brain, heart), genomics, multi-omics, ...
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Chronic noncommunicable diseases, infectious diseases, oncology, ...
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Drug discovery, and biopharma industry
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...
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Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)
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And more
ML x Health Speakers
ML x FINANCE
11-14 August, 2022
Oxford St Catherine's College & Online

Building on the topics covered in ML fundamentals module, the Finance module will continue and cover the following topics:
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Statistical / probabilistic ML (e.g., Bayesian ML, Gaussian processes, approximate inference, modelling uncertainty, learning from large data, ...)
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Advanced topics in representation learning (e.g., learning with no labels, representation learning in time series, text, and multi-modal data)
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Natural language processing (e.g., large language models, multi-lingual NLP, sentiment/opinion mining, fact checking / false news, misinformation detection, ...)
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Reinforcement learning
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Knowledge graphs
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Knowledge-aware ML
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Symbolic reasoning
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Neuro-symbolic AI
Applied talks on ML in/for:
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Financial time series (e.g., standard models, Gaussian processes, representation learning, ...)
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Building market simulators
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Trading and hedging
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Insurance, asset management, emerging risks
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Financial inclusion and economic prosperity
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ESG
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...
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Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)
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And more