330+ civil and public servants have completed the AI Fundamentals course since 2024.

Professor Tom Coates is a Mathematics Professor at ³Ô¹ÏºÚÁÏ. He was formerly seconded to the Office of the Chief Scientific Advisor and has delivered the AI Fundamentals course to hundreds of civil servants in several government departments.

Dr Sara Veneziale is a Chapman-Schmidt fellow at the I-X Centre for AI in Science and the Department of Mathematics, and is one of the AI Fundamentals course instructors. 

Course Information

Date of next cohort: 2nd October - 27th November 2026 (break 30th Oct) 

Application deadline: Please complete the application form and submit to ³Ô¹ÏºÚÁÏ Policy Forum (the.forum@imperial.ac.uk) by 23:59 on Friday 4 September 2026. 

Cost: The fee for the course is £750 (no VAT added)

Duration: 8 weeks   

Time commitment: 8 x 3-hour sessions   

Location: South Kensington Campus, ³Ô¹ÏºÚÁÏ   

Who should apply? This course is for civil servants of any grade involved in AI regulation, strategy or systems. No prior AI or computer science training required.

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About the programme

°Õ³ó±ð AI Fundamentals programme is designed for civil servants of any grade involved in AI regulation, strategy, or systems. With the rapid proliferation of AI tools and systems, governments face significant public policy challenges and opportunities. Civil servants must navigate uncertainties around regulating AI’s societal use while harnessing its transformative potential to improve the design and delivery of public services. Central government departments occupy a pivotal role, both as regulators of AI adoption and as major users and developers of AI technologies across their operations and public services.  

The course will empower participants to act as informed ‘expert customers’ in the development and deployment of AI systems. As the UK Government rolls out its AI Opportunities Action Plan and its vision for modern digital government, this course is both timely and tailored for Civil Servants.   

With no prior AI or computer science training required, participants will gain access to ³Ô¹ÏºÚÁÏ’s globally renowned expertise in AI research and education. Combining interdisciplinary teaching, hands-on experience, and real-world case studies, the course delivers actionable knowledge to support the safe and impactful deployment of AI in public services.  

The programme will consist of eight training sessions targeted specifically at policymakers. The time commitment requested from participants is one half-day per week over the course of an eight-week period. 

"Highly recommend this course for anyone working around the intersection between AI and public policy. The course was structured really well with a nice balance between discussion-filled presentations, guest speakers and exercises." - Civil Servant, AI Security Institute

Benefits

  • World-Class Expertise: Learn from ³Ô¹ÏºÚÁÏ's globally renowned faculty, leaders in AI research and application across engineering, medicine, and science. 
  • Tailored for Public Sector: Gain practical insights through a curriculum co-designed with civil servants to address the unique challenges of public sector AI deployment. 
  • Interdisciplinary Learning: Understand AI's integration with policy, ethics, and governance through real-world case studies and applications. 
  • Strategic Knowledge: Master fundamental AI concepts, resource requirements, and program design for implementing AI in government contexts. 
  • Risk and Benefit Assessment: Develop the ability to evaluate the risks and benefits of various AI technologies in public services. 
  • Hands-On Experience: Gain practical skills with AI systems, including working with advanced tools like Large Language Models. 

Programme outline

Week 1: Fundamental concepts

Lecture: Basic concepts in AI and machine learning, the AI triad, history of AI, opportunities and transformation, generative vs discriminative models, training data, types of learning.  

Seminar: training a simple AI model. 

Week 2: Large Language Models

Lecture: Neural networks, deep learning, examples of AI models and neural networks in computer vision, training LLMs, natural language processing (NLP), the foundation model paradigm, biases, hallucinations, memorisation, and policy implications.  

Seminar: hands-on session. 

Week 3: Training Foundation Models and agents

Lecture: Foundation model training, pre- and post-training, supervised fine tuning, reinforcement learning, inferences. 

Seminar: AI and geopolitics 

Week 4: Agents, testing, and evaluations

Lecture: Agentic AI, tool use, retrieval augmented generation (RAG), evaluating LLMs and agents.  

Seminar: AI alignment. 

Week 5: Security risks to and from AI systems

Lecture: Challenges in deploying AI systems, risks from malicious use, systemic risks from AI systems, security risks to AI systems.  

Seminar: computing at scale.  

Week 6: Understanding todays and tomorrow’s AI

Lecture: Explainable AI, world models, physical intelligence, horizon scanning.  

Seminar: AI and liability. 

Week 7: AI governance and regulation

Lecture: Key trade-offs in AI regulation, UK public attitudes to AI, the UK's regulatory approach, new state institutions (Alan Turing Institute, AI Security Institute), the UK policy timeline, international coordination and the AI Summit series, and comparative approaches in the EU, US and China.  

Seminar: group project work. 

Week 8: Project presentations and panel discussion

 A panel discussion with perspectives from academia, industry and government on how AI will continue to develop in the medium-long term and what it means for policymakers.  

"Absolutely excellent course...As a non-technical personal, I left the course feeling more informed and confident in how models are developed, the key technical questions, and how I can play the knowledge gained into comprehensive policy advice." - Civil Servant, DSIT

Faculty

  • Tom Coates

    Personal details

    Tom Coates Programme Lead

    Biography

    Prof. Tom Coates is a Professor of Mathematics at ³Ô¹ÏºÚÁÏ. His research group works in algebraic geometry and large-scale computational algebra, building a Periodic Table for shapes, by combining new methods in geometry with cluster-scale computation, data mining, and machine learning. Prof. Coates has been on part-time secondment to the Office of the Chief Scientific Adviser since 2022 .    

  • Sara Veneziale

    Personal details

    Sara Veneziale Instructor

    Biography

    Dr Sara Veneziale is a Chapman-Schmidt fellow at the I-X Centre for AI in Science and the Department of Mathematics at ³Ô¹ÏºÚÁÏ. Her research focusses on using AI to discover and prove new results in mathematics, and on the high-dimensional geometry that underpins Large Language Models.  

  • Luca Andolfi

    Personal details

    Luca Andolfi Research Fellow

    Biography

    Dr Luca Andolfi is a Research Fellow at ³Ô¹ÏºÚÁÏ, specialising in the intersection of formal logic, knowledge representation, and machine learning. His research focuses on neuro-symbolic integration, with a particular focus on investigating methods for learning semantically meaningful concepts that underpin intermediate reasoning processes in neuro-symbolic architectures. By enabling more interpretable and robust forms of reasoning, his work contributes to the development of transparent and trustworthy AI systems.

  • Pranav Mamidanna

    Personal details

    Pranav Mamidanna Research Fellow

    Biography

    Dr Pranav Mamidanna is a Research Fellow at ³Ô¹ÏºÚÁÏ, where he works at the boundary of AI and neuroscience. His research brings together the mathematical models traditionally used to simulate physical and biological systems with the representational power of modern AI — and pushes these hybrid approaches toward clinical translation.