Artificial Intelligence

Module overview: 

This module explores the dual paradigms of natural and computational intelligence by integrating theoretical insights with practical applications. It examines the biological inspirations behind intelligent behaviour and contrasts these with computational approaches based on algorithmic design and computability.

Topics include the foundational paradigms of AI, cellular and developmental systems, evolutionary computation and algorithms, as well as behavioural, collective, and swarm intelligence, culminating in the study of hybrid systems that merge natural and computational methodologies.

The course is designed to provide you with a comprehensive understanding of how bio-inspired techniques can be applied to solve complex, multidisciplinary challenges, while addressing issues of computational efficiency, system feasibility, and the trade-offs inherent in AI system design.

This module is carefully structured to ensure that you not only gain theoretical insights but also develop practical and analytical skills necessary for advancing the field of AI through a synthesis of natural and computational approaches.

Module overview: 

This module provides a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML), covering foundational theories, principles, and mathematical underpinnings.

You will explore intelligent agents, problem-solving through search techniques, knowledge representation, probabilistic reasoning, and optimisation methods in AI. The second half of the module focuses on machine learning methodologies, including supervised and unsupervised learning, feature engineering, and model evaluation.

Through practical implementations, you will develop skills in building intelligent systems, critically evaluating their effectiveness, and applying computational techniques to solve real-world challenges.

This module is essential for pursuing AI-related careers, as it lays the groundwork for more advanced studies in deep learning, generative AI, and Applied AI engineering.

Module overview:

This module provides an in-depth exploration of deep learning and generative AI, focusing on the theoretical foundations, state-of-the-art architectures, and practical implementations of advanced neural networks. You will study the mathematical and computational principles underlying deep learning, including backpropagation, optimisation strategies, and regularisation techniques. You will gain hands-on experience with industry-standard frameworks such as TensorFlow and PyTorch to design, train, and optimise deep learning models.

The second half of the module delves into generative AI, covering key architectures such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models. Special attention will be given to Transformers and Large Language Models (LLMs), which power state-of-the-art generative AI systems. You will critically assess the impact, ethical implications, and computational challenges associated with deploying these models at scale.

This module equips you with the theoretical knowledge and practical skills needed to innovate in the fields of computer vision, natural language processing, and creative AI applications.

Module overview:

The AI Systems Engineering module provides you with a comprehensive understanding of the methodologies, frameworks, and best practices for designing, deploying, and managing AI systems at scale. This module equips you with the practical and theoretical knowledge required to build end-to-end AI solutions that are scalable, reliable, and reproducible across various industries.

Through a structured learning approach, you will explore key topics such as data engineering, AI development frameworks, model development, AI deployment strategies, monitoring, and MLOps. The module also incorporates real-world case studies in healthcare and agriculture, allowing students to apply AI system engineering principles to domain-specific challenges.

A strong emphasis is placed on Responsible AI, covering ethical considerations, transparency, fairness, and compliance with data privacy regulations. By the end of this module, you will have the technical expertise and critical thinking skills needed to develop and manage AI solutions that adhere to industry standards and best practices.

This module is essential for pursuing careers in AI engineering, MLOps, data science, and AI system architecture, providing you with hands-on experience in tools such as DVC, MLflow, Kubernetes, Docker, TensorFlow Serving, and monitoring frameworks.

Module overview:

You will explore a topic of your choosing, based on your own interests as agreed and supported by a member of the academic team and produce a product and supporting report.

The project provides an opportunity for you to research and deliver a significant piece of individual work that incorporates the practical and analytical skills presented in the programme.

The final year project will enable you to explore a topic of you choice. There are four project types:

• Student-defined
• Academic-defined (research based)
• Industry-defined
• Social enterprise