Introduction to AI Frameworks

Date & Time: Friday 27 November 2026, 09:30 - 16:30 GMT

Location: Edinburgh

This course will primarily introduce the PyTorch framework, the most popular Python AI framework used for implementing neural networks and other applied AI applications. The course will also briefly cover other widely used AI frameworks such as JAX and TensorFlow, highlighting when these alternative frameworks may be more suitable for the implementation of work.

This in-person course is aimed at anyone who has introductory level knowledge of neural networks and their usage. Attendees will get the most out of this course if they have basic Python and linear algebra knowledge. Familiarity with the use of Python's NumPy library will be useful. We also recommend that people are already famiiar with the concepts described in the Pilot-UKAIFA Demystifying AI webinar.

Further information and registration for this course can be found here.

Ethical Issues for AI Solutions

Date & Time: Wednesday 9 December 2026, 09:30 - 16:30 GMT

Location: Edinburgh

This one-day, in-person course examines current ethical challenges raised by the development and deployment of AI. Drawing on real-world examples, it will equip participants to critically discuss and evaluate key issues and the trade-offs they create for developers, organisations and users in a balanced, evidence-informed way.

Topics include algorithmic bias and discrimination, environmental impacts, intellectual property and data governance, workplace and labour impacts, and the potential effects of automation on skills and deskilling of humans.

The course is designed for anyone interested in these questions and assumes no technical background.

Further information and registration for this course can be found here.

Introduction to Deep Neural Networks (DNNs)

Date & Time: Tuesday 27 October 2026, 09:30 - 16:30 GMT

Location: Edinburgh

This course is aimed at anyone who has awareness of AI and machine learning and may even have introductory level knowledge of neural networks and their usage. It will introduce the fundamental concepts of Deep Neural Networks (DNNs) and the reasons behind different DNN architectures. You will learn about most common DNN architectures such as FeedForward Neural Networks (FNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and the Transformer architecture. The main emphasis of this course is on understanding different DNN architectures, their strengths and weaknesses and when to apply the most suitable architecture when designing an AI solution to a specific problem.

This in-person course is an extended version of the recent Pilot-UKAIFA webinar on DNNs with additional practical exercise, opportunities for discussion and some additional content. The level of the one-day course will be similar to the webinar.

The course materials can be found here.

Webinar: Introduction to Deep Neural Networks (DNNs)

Date & Time: Monday 7 September 2026, 11:30 - 13:00 BST

This webinar is aimed at anyone who has awareness of AI and machine learning and may even have introductory level knowledge of neural networks and their usage. It will introduce the fundamental concepts of Deep Neural Networks (DNNs) and the reasons behind different DNN architectures. You will learn about most common DNN architectures such as FeedForward Neural Networks (FNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and the Transformer architecture. The main emphasis of this course is on understanding different DNN architectures, their strengths and weaknesses and when to apply the most suitable architecture when designing an AI solution to a specific problem.

The slides of this webinar can be found here.

The recording of this webinar is available on the Pilot-UKAIFA Training channel on Youtube: see the webinar's video.