The University of Edinburgh is at the forefront of AI research and education, building on a rich legacy that spans over six decades our position as a global leader in AI innovation and now, through Executive Education, we are empowering professionals across industries to harness the transformative potential of AI.

The University of Edinburgh’s six-week online course is designed to empower senior leaders and decision makers to harness the transformational power of Artificial Intelligence (AI) and Generative AI and drive innovation within their organisations.

This flexible course provides, tools and leadership skills to deliver innovation and lead in a digital age.

Our course brings together the unparalleled expertise of the University of Edinburgh's Business School, School of Informatics, The Bayes Centre, Edinburgh Futures Institute and Edinburgh Law School. This unique collaboration delivers cutting edge-insights into Artificial Intelligence, data driven innovation, ethics and regulation.

Why join?

Key benefits of the University of Edinburgh AI course:

  • Strategic AI leadership – gain the expertise to evaluate AI’s impact across industries and develop actionable strategies for responsible adoption.
  • Interdisciplinary insights – learn from world-leading academics across Informatics, the Business School, and Edinburgh Futures Institute, providing a uniquely holistic perspective on AI’s technical, business and societal dimensions.
  • Ethical and responsible AI – build a critical understanding of AI’s societal implications, ethics and governance to ensure innovation aligns with public good and regulatory frameworks.
  • Foundations of AI & machine learning – develop a clear, practical grasp of key AI concepts, including machine learning, neural networks and deep learning, without requiring a technical background.
  • Practical business application – master the skills to integrate AI into your organisation, create a compelling business case and drive innovation with confidence.
  • Access to a world-class AI ecosystem – engage with Edinburgh’s globally renowned AI research community, industry leaders and cutting-edge developments, shaping the future of AI.
  • Expert-led learning experience – gain insights from top AI researchers and industry practitioners, combining academic excellence with real-world relevance.
  • Exclusive professional network – join a high-calibre network of peers, faculty and AI experts, fostering long-term connections within Edinburgh’s thriving AI and tech ecosystem.
  • University of Edinburgh digital badge - recognises your expertise in AI.

Course outline

  • Flexible format- the course employs a blended learning approach, comprising three hours of mandatory self-paced online material per week, with up to an additional hour of optional resources, complemented by a weekly one-hour live session.
  • Interactive learning - live Q&A sessions with AI experts.
  • Comprehensive curriculum - covers AI fundamentals, generative AI, ethical considerations and AI governance.

Course presenters

  • Dr Valerio Restocchi – Reader in Behavioural Data Science at the School of Informatics
  • Dr Shama Rahman – Innovation Fellow, University of Edinburgh School of Informatics
  • Dr Ben Moews – Lecturer in Predictive Analysis, University of Edinburgh Business School
  • Dr Adam Ntakaris – Lecturer in Financial Technology, University of Edinburgh Business School
  • Hermione Hague – Lead Tutor on IP Elective on the Diploma with the Law School
  • Professor Burkhard Schafer – Personal Chair of Computational Legal Theory, University of Edinburgh School of Law

Course modules

  • Meet the course instructors and fellow participants
  • Platform familiarisation
  • Course overview and expectations

This module is a comprehensive introduction to AI fundamentals, covering machine learning types, data handling, prediction methods and model evaluation metrics.

  • Fundamentals of AI and machine learning
  • Types of machine learning: supervised (from examples), reinforcement (through trial and rewards) and unsupervised learning (on its own)
  • Working with data: representing data, categorical vs real valued attributes, features
  • Brief overview of prediction methods and examples of how to use them in a business context
  • Measuring success of models: accuracy, precision and recall

This module introduces core concepts in deep learning and generative AI.

  • Overview of neural network types, including Feedforward, Recurring Neural Networks, and Convolutional Neural Networks
  • Distinctions between machine learning, deep learning, and reinforcement learning
  • Introduction to generative AI and its applications in large language models
  • Use of generative AI tools for market research and strategy development
  • Application of generative tools in narrative construction and visual storytelling

This module provides an in-depth analysis of the crucial elements to the reliability and trustworthiness of AI systems across various industries.

  • Review of key risks relating to the reliability of AI systems, statistical accuracy, data accuracy, and the validity of data inputs and generated outputs
  • Understanding bias risks and concepts, as well as unintended outcomes for vulnerable customers
  • Understanding transparency: A deep dive into transparency in context (audiences as clients, financial consumers, regulators, suppliers, and other stakeholders)
  • Best practice approaches to explainability and a consideration of explainability frameworks

This module explores the critical challenges of bias in financial services AI systems, focusing on strategies for ensuring fairness, accountability, and effective data management.

  • An in-depth examination of bias and causal modelling in financial decision-making, equipping leaders with tools to identify and address AI-driven biases
  • Practical approaches to assessing and mitigating AI bias, drawing on frameworks like fairness monitoring and strategic planning for responsible AI deployment
  • Engagement with real-world examples, including successful AI implementations and notable failures, to highlight the impact of bias on customer trust, regulatory compliance, and reputational risk
  • Review of key data risks and the duty to protect consumers, emphasising actionable strategies to safeguard against AI bias and ensure consumer trust

This module covers key aspects of AI regulation and AI governance, both nationally and internationally. We will discuss case studies from “compliance technology” and legal tech to examine some of the legal risks associated with the use of AI systems and explore strategies for mitigating them. We will cover some of the most important legal regimes, but also go beyond legal compliance to discuss questions of AI ethics, industry standards and design-based compliance approaches.

  • Understanding the emerging regulatory framework in the UK, the EU and globally (UK AI White Paper; EU AI Act)
  • AI and data protection law (UK Data Protection Act 2018
  • AI harms, fault attribution, and liability
  • Relevance and state of standards relating to bias and other risk controls
  • Ethical AI: Impact Assessments, tools and policies (AI4People ethics toolkit; Montreal Declaration on ethical AI; UK gov. Responsible AI toolkit)
  • Understanding monitoring and oversight throughout the AI lifecycle and expectations at each stage

This module covers IP aspects of AI systems, including ownership and licensing and managing risks of infringement. We will consider typical ways in which IP risks are allocated in contract and residual risks and mitigation measures. This week, we then bring considerations from AI regulation, governance and IP together in developing AI Policies for the development and use of AI.

  • Intellectual property considerations:
    • Ownership and infringement issues: use of training data, inputs and outputs in the light of recent UK AI case law
    • Regulatory disclosure requirements for using copyright training material in developing AI (UK and EU)
  • Intellectual property risk strategies:
    • Considering approaches for the development and use of AI systems and ensuring IP and confidential information are protected
    • Reviewing typical contractual approaches for ownership, licensing and allocation of IP risks
    • Developing AI Policies for the development and use of AI, considering IP, AI regulation, governance and risks

Who should enrol?

This leading online course is specifically designed for senior and middle managers, as well as senior leadership teams, aiming to enhance their strategic insight and guide their organisations through the intricacies of digital transformation and AI integration.

Ideal for visionary leaders committed to leveraging AI for competitive advantage, participants will explore both the implementation of AI technologies and strategies to drive transformation effectively.

This course offers a unique opportunity to share ideas and learn from other delegates within an elite global network, fostering collaboration and cross-industry insight that enriches the practical and strategic deployment of AI in business settings.

Course dates

The course content will be released on a week-by-week basis, starting with an introductory module on Monday 8 September 2025.

A series of 1-hour live sessions will take place once per weekly module, starting 6pm UK time:

  • Module 1 – Thursday 18 September 2025
  • Module 2 – Thursday 25 September 2025
  • Module 3 – Thursday 2 October 2025
  • Module 4 – Thursday 9 October 2025
  • Module 5 – Thursday 16 October 2025
  • Module 6 – Thursday 23 October 2025

Register your interest today and gain the knowledge to transform your organisation with the power of AI.

AI is the future. Take the lead.

Fees

£2,497