Summer 2027Oxford + onlineTheory & applied70% research; 30% engineeringCertification

Foundation Models

Beyond language

Shared principles across different data languages.

I · Foundations

Representation learning
Transformers
Emerging architectures
Tokenization
Evaluation
Scaling laws
Multimodality
Transfer

Feeding into five verticals

Part II

Health, Biology and Scientific Systems

How models learn across clinical, biological and experimental scientific systems.

Topics

Imaging
EHR & Patient Trajectories
Wearables & Biosignals
Genomics
Proteins & Proteomics
Chemistry & Materials
Drug discovery
Autonomous experimentation

Part III

Financial Markets and Economic Systems

How models learn in adaptive and non-stationary environments.

Topics

Time-series models
limit order books
forecasting
regime change
market simulation and economic agents

Part IV

Code, Mathematics and Formal Systems

How models reason within executable and verifiable languages.

Topics

Code models
program synthesis
mathematical reasoning
theorem proving
verification and RL with verifiable feedback

Part V

Music, Audio and Acoustic Systems

How models learn from sound, structure and composition over time.

Topics

Audio representation
music modelling
generation
separation
restoration and multimodal audio intelligence

Part VI

Robotics and the Physical World

How models connect perception, prediction and action.

Topics

World models
robotics
industrial systems
digital twins and physical intelligence

Audience & outcomes

Who it's for

Researchers, engineers, scientists and technical leaders across health, biology, chemistry, software, finance, audio, robotics and the physical sciences. Participants should have a working understanding of machine learning.

What you'll gain

A unified understanding of foundation models beyond language
Knowledge of architectures for diverse data and systems
Insight into scaling, multimodality, transfer and adaptation
Familiarity with leading research across major domains
Methods for evaluating robustness, uncertainty and validity
A framework for comparing foundation models across domains

Certificate of Participation

Awarded to every participant.

Skills accreditation

Verified certificate, subject to passing the exam.

/ OxML 2027 — Oxford

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What you'll gain

A unified understanding of foundation models beyond language

Knowledge of architectures for diverse data and systems

Insight into scaling, multimodality, transfer and adaptation

Familiarity with leading research across major domains

Methods for evaluating robustness, uncertainty and validity

A framework for comparing foundation models across domains