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Oxford Machine Learning Summer School
OxML 2023

Program & Speakers

Mathematical Institute
University of Oxford


Andrew Wiles Building, Radcliffe Observatory Quarter,
Woodstock Road, Oxford, OX2 6GG

ML Fundamentals

MLx FUNDAMENTALS 

8-10 May

Online

MLx CASES

June 2023

Online

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MLx Fundamentals:

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:

 

  • Linear Algebra and Mathematics of machine learning

  • Optimisation

  • Fundamentals of statistical / probabilistic ML

  • Fundamentals of representation / deep learning

  • and more

MLx Cases:

The aim of ML x Cases track is to provide you with a training on real-world issues and processes related to ML development/implementation process. This will range from efficient and repeatable approaches to data collection, enrichment and cleaning, and labelling, to transfer learning use cases of pre-trained SOTA models and their fine-tuning to achieve good performance on a domain-specific task. We will run ~5 different cases, led by experienced ML / data scientists, supported by TAs to help make the sessions interactive.
At the end of the ML x Cases, participants will learn useful concepts on:

  • Frame a problem as an ML problem

  • Leveraging appropriate toolboxes

  • Knowing which approach typically works best depending on the types of use cases

  • Defining what performance metrics to choose

  • Experimental setups for a performant model, while tracking and documenting experiments with MLFlow

  • Forming a naive baseline to more sophisticated experiments

  • Interpreting model results (e.g., under/overfitting and ways to remediate it).

  • Feedback loops and allowing the system to collect information from user inputs.

  • and more

Speakers: MLx Fundamentals & MLx Cases

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Haitham Bou Ammar

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RL Team Leader

Huawei Research

Bayesian Optimisation

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Eduardo C. Garrido-Merchán

Research Scientist

Universidad Pontificia Comillas

GPT4

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Yali Du

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  • Website

Lecturer in AI

King's College London

Optimisation fundamentals

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Vincent Moens

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Research engineer, TorchRL developer

Meta

MLx Cases

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Matthieu Zimmer

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Senior Research Scientist 

Huawei Research

fundamentals of RL

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Khémon BEH 

  • Webpage

Founder & CEO

Quickscale.ai

MLx Cases 

Rasul Tutunov

Rasul Tutunov

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Research Scientist 

Huawei Research

Maths fundamentals

MLx FINANCE &
NLP

Stock Market Down
MLx finance

Building on the topics covered in ML fundamentals module, the Finance module will continue and cover the following topics:

  • Statistical / probabilistic ML (e.g., Bayesian ML, Gaussian processes, approximate inference, modelling uncertainty, learning from large data, ...)

  • Advanced topics in representation learning (e.g., learning with no labels, representation learning in time series, text, and multi-modal data)

  • Natural language processing (e.g., large language models, multi-lingual NLP, sentiment/opinion mining, fact checking / false news, misinformation detection, ...)

  • Reinforcement learning

  • Knowledge graphs

  • Knowledge-aware ML

  • Symbolic reasoning

  • Neuro-symbolic AI

Applied talks on ML in/for:

  • Financial time series (e.g., standard models, Gaussian processes, representation learning, ...)

  • Building market simulators

  • Trading and hedging

  • Insurance, asset management, emerging risks

  • Financial inclusion and economic prosperity

  • ESG

  • ...

  • Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)

  • And more

SPEAKERS: MLx FINANCE & NLP

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Rama Cont

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Professor of Mathematical Finance 

University of Oxford

Quantitative finance, ML for building market simulators

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Stefan Zohren

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Deputy Director of Oxford-Man Institute

University of Oxford

Representation learning & (financial) time series

Blanka Horvath

Blanka Horvath

  • Google Scholar
  • Website

Professor in Oxford Math Finance Group

University of Oxford

Market Simulators, Deep Hedging

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Svetlana Bryzgalova

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Assistant Professor of Finance

London Business School

ML, capital markets and  factor investing

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Rahul Savani 

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Professor of Computer Science

University of Liverpool

RL in Finance & Automated Trading

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Mihai Cucuringu

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Associate Professor of Statistics

University of Oxford

Networks, statistical ML, and quant. finance

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Diyi Yang

  • Google Scholar
  • Website

Assistance Professor 

Stanford University

Multi-lingual NLP 

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Pasquale Minervini

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Lecturer in NLP

University of Edinburgh,

UCL

Advanced topics in ML + NLP

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Edward Grefenstette 

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Head of ML at Cohere,

 Honorary Professor at UCL

NLP & generation, machine reasoning, meta-learning

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He He

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Assistant Professor of Comp. Science

NYU

Robustness & truthfulness of NLP models

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Stephen Clark

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Head of AI

Quantinuum

Quantum NLP

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Ryan Cotterell

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Assistant Professor of Computer Science

ETH Zürich

Computational linguistics, NLP & ML

MLx HEALTH

Brain Scans
ML x Health

Building on the topics covered in ML fundamentals module, the Health module will continue and cover the following topics:

  • Statistical / probabilistic ML (e.g., Bayesian ML, causal inference, approximate inference, modelling uncertainty, ...)

  • Advanced topics in representation learning (e.g., learning with little or nor supervision, self-supervised learning, multi-modal representation learning, ...)

  • Graph neural networks, and geometrical deep learning

  • Computer vision

  • Knowledge graphs

  • Knowledge-aware ML

  • Symbolic reasoning,

  • Neuro-symbolic AI

Applied talks on ML in/for:

  • EHR, imaging (e.g., brain, heart), genomics, multi-omics, ...

  • Chronic noncommunicable diseases, infectious diseases, oncology, ...

  • Drug discovery, and biopharma industry

  • Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)

  • And more

Speakers: MLx Health

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Gitta Kutyniok

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Professor of Applied Maths, Chair of Mathematical Foundations of AI

Ludwig Maximilian University of Munich

Mathematics & theory of ML/DL

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Mireia Crispin

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Lecturer in Integrated Cancer Medicine

University of Cambridge

 

ML, multi-omics, and oncology

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Kyunghyun Cho

  • Google Scholar
  • Webpage

Associate professor of computer science & data science, NYU

CIFAR Fellow,

Senior Director of Frontier Research, Genentech

Advanced topics in RL & ML for comp. bio.

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Ishan Misra

  • Google Scholar
  • Webpage

Research Scientist 

Facebook AI Research (FAIR)

 

ML, computer vision, and learning with reduced supervision

Louis-Philippe Morency

Louis-Philippe Morency

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Associate Professor of Computer Science

Carnegie Mellon University

Multi-modal rep. learning

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Cheng Zhang

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Principal Researcher

Microsoft Research Cambridge

Probabilistic ML & causal ML

Munmun De Choudhury

Munmun De Choudhury

  • Google Scholar
  • Webpage

Associate Professor in the School of Interactive Computing

Georgia Tech

 

ML, digital health, mental health & wellbeing

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Marwin Segler

  • Google Scholar
  • Webpage

Principal Researcher 

Microsoft Research

ML for drug discovery

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Kazem Rahimi

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Professor of Cardiovascular Medicine

University of Oxford

ML for population health, & chronic diseases

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Ali Eslami

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Research Scientist 

Google DeepMind

Advanced topics in Representation Learning

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