Online Winter School | 6-8 January 2027 | University of Cambridge
Are you ready to move beyond AI hype and generalist tools and start solving your real- world problems in your work or research?
Over 3 intensive days, you will gain practical mastery in unsupervised machine learning and large language models, learn how to build agentic and autonomous systems, and walk away with a toolbox to successfully navigate machine learning problems.
This course is built for active learning through live demonstrations, hands-on practical activities, and collaborative group projects, you will work directly alongside course tutors and our dedicated team of teaching assistants. This is not your average online course or lecture series. It is hosted by Cambridge Centre for Data-Driven Discovery and Professional and Continuing Education, University of Cambridge.
Immersive learning programme led by educational experts—built on practical, highly interactive participation
Overview
Apply by: Monday 21st December 2026
Start date: Wednesday 6th January 2027
Finish date: Friday 8th January 2027
Time commitment: 3 full days
Format: live online, UK time
Payments: varying from £90 to £650
Key Features
- Keynotes from leaders in academia and industry
- A programme packed with hands-on practical activities and a hackathon
- Unsupervised Machine Learning, Foundations of LLMs, Transformer Architectures, APIs to LLMs, Automating Complex Tasks, Agentic AI, Challenges and pitfalls of real-world ML problems
Who should enrol?
This winter school is intended for professionals, researchers and postgraduate students across industry, academia and the public and third sectors who want to explore how AI can address practical problems in their work or research. It will particularly suit those seeking to connect AI concepts with real applications through demonstrations, guided exercises and collaborative problem-solving.
Prerequisites
To be able to fully engage with the course, the prerequisites are as follows:
- A foundational understanding of core AI/ML concepts, good data literacy, and general awareness and interest of current AI technologies.
- Good familiarity with Python
- Experience using LLMs (e.g. on user-friendly interfaces like ChatGPT)
- Google account with access to Google Drive and Google Colab (e.g. normal Gmail)
- An existing GitHub account, basic familiarity with Git.
Attendance fee
£90 Students, LMIC, Unemployed
£280 Academia: postdocs, academics, professional services, emeritus faculty
£300 Government and Charities
£650 Industry and other organisations
Registration
Please register your attendance here
Deadline for registrations is 5 pm (UK) Monday 21st December.
Payment needs to be made separately here.
Deadline for payments is 5 pm (UK) Monday 21st December. Payment is required to confirm your place.
(UoC members who are self‑funding may proceed with the booking as an external customer, using their personal payment card, provided they do not intend to reclaim the participation fee through expenses. If they wish to reclaim, an internal Trading invoice and cross-charge needs to be arranged between departments.)
Certificate of Attendance
The Winter School is able to offer a certificate of attendance to all participants who participate fully across the three days.
Speakers and Tutors
Professor Zoe Kourtzi Professor of Computational Cognitive Neuroscience, Department of Psychology, University of Cambridge
Professor Richard Turner Professor of Machine Learning, Department of Engineering, University of Cambridge. Specialising in AI for weather forecasting
Professor Shijing Sun Associate Professor in Materials Science, Department of Materials Science and Metallurgy, University of Cambridge. Specialising in human-AI-robot collaborations and autonomous laboratories
Dr Soumya Banerjee Machine learning researcher and data scientist; Visiting Lecturer, University of Cambridge
Radzim Sendyka Machine Learning Engineer, Department of Computer Science and Technology, University of Cambridge
Professor Michael Roberts Professor of Applied AI, Department of Applied Mathematics and Theoretical Physics and Department of Medicine, University of Cambridge
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