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
MLx FUNDAMENTALS
8-10 May
Online
MLx CASES
June 2023
Online

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

Rasul Tutunov
Research Scientist
Huawei Research
Maths fundamentals
MLx FINANCE &
NLP
8-11 July, 2023

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

Rahul Savani
Professor of Computer Science
University of Liverpool
RL in Finance & Automated Trading

Mihai Cucuringu
Associate Professor of Statistics
University of Oxford
Networks, statistical ML, and quant. finance

Diyi Yang
Assistance Professor
Stanford University
Multi-lingual NLP

Pasquale Minervini
Lecturer in NLP
University of Edinburgh,
UCL
Advanced topics in ML + NLP

Edward Grefenstette
Head of ML at Cohere,
Honorary Professor at UCL
NLP & generation, machine reasoning, meta-learning

He He
Assistant Professor of Comp. Science
NYU
Robustness & truthfulness of NLP models

Stephen Clark
Head of AI
Quantinuum
Quantum NLP

Ryan Cotterell
Assistant Professor of Computer Science
ETH Zürich
Computational linguistics, NLP & ML
MLx HEALTH
13-16 July, 2023

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