Computer Science (Artificial Intelligence) BSc (Hons)

Through practical workshops and collaborative activities, you’ll learn how to communicate clearly, work with others respectfully and make purposeful use of technology in your learning. You’ll explore how to find and evaluate reliable information, use academic sources correctly, and reference your work following university guidelines.

You’ll also reflect on your own strengths, goals and areas for development. This will help you build self-awareness, support your wellbeing and develop as an independent learner.

Assessment includes a portfolio of tasks that demonstrate your developing academic skills where you will communicate your ideas clearly and appropriately. This may include workshop activities, short pieces of writing, or oral presentations.

You’ll learn about common network structures, communication protocols and the principles that allow devices to connect and share information. The module also examines the risks that networks face, including security threats and system vulnerabilities, and how these can be reduced through protective strategies.

You’ll also explore emerging technologies in networking and cybersecurity, and consider how they shape the way organisations and society use computing systems.

Throughout the module, you’ll apply what you learn to practical scenarios. This will help you understand how networking and security concepts can be used to address real-world computing challenges.

This module will provide you with a strong foundation in networking and security that will support your future study in computing.

You’ll explore key programming concepts, including control structures, algorithms and object-oriented programming. You’ll also learn how to choose and use appropriate data structures to solve different computational problems.

As you progress, you’ll develop practical coding skills and gain experience in designing, building and testing applications. This includes working with both console-based programs and graphical user interface (GUI) applications, helping you understand how different types of software are created.

The module focuses on hands-on learning, giving you opportunities to practise writing code and developing your own solutions to programming challenges.

This module will prepare you for more advanced study in software development, algorithm design and specialised areas of computing.

You’ll explore the main types of machine learning, including supervised, unsupervised and reinforcement learning. The module also introduces the foundations of neural networks and the mathematical ideas that support them.

Through practical activities, you’ll learn how to build and train machine learning models using programming tools. You’ll also explore how models are tested, evaluated and improved to produce more accurate results.

As you develop your skills, you’ll gain experience working with the building blocks used to create neural networks and other AI systems.

You’ll explore how computer systems operate, examining hardware components, processor architectures, and the way these elements work together to run programs and manage tasks.

You’ll also investigate key operating system functions, including process management, memory management, and file systems. This will help you understand how the system coordinates resources and keeps everything running smoothly.

A major part of this module is learning to write low‑level programs using assembly language. You’ll create simple system‑level programs and see how software interacts directly with hardware and system resources.

This module will see you learn structured approaches to software development, including requirements analysis, design methods, implementation techniques, and testing strategies. These skills will guide you as you design, build, and deploy relational databases that meet real user and organisational needs.

You'll develop professional practices such as version control, clear documentation, and Agile ways of working. As you create functional databases, you’ll learn how to apply appropriate security measures and access controls to protect data effectively.

You'll create database schemas, identify security threats and apply safeguards, use established software development and quality assurance techniques, and understand how software engineering principles guide the entire process of database creation.

Gain a practical introduction to how images are represented, processed, and understood by modern deep learning systems. You’ll explore how visual data are captured and transformed, and how computational models extract meaning from pixels.

You’ll investigate the neural network architectures that make this possible, with a focus on convolutional networks and the role they play in solving real computer vision challenges. The aim is to help you understand not just how these models work, but why they work.

Through applied exercises, you’ll implement techniques for processing visual information and build practical solutions to visual computing problems. Across this module, you’ll develop the confidence to explain key concepts in image representation, apply deep learning methods, and choose appropriate techniques for a wide range of computer vision tasks.

This module introduces you to the core ideas behind knowledge representation, reasoning systems, and natural language processing (NLP) technologies.

You’ll learn how computers process and analyse text, uncovering the steps involved in understanding meaning within written language.

You’ll investigate key NLP models and techniques, including language models, natural language understanding, and text generation. This helps you see how modern systems interpret text, respond to queries, and produce language that feels natural.

Through hands-on work, you’ll implement methods for analysing textual data and develop solutions to real‑world language processing challenges.

This module will see you gain a practical look at how large language models and autonomous agents work.

You'll explore what sits behind these systems, how they process information, and how they can be adapted for different tasks and industries.

Through practical activities, you'll building simple agents, with attention given to keeping systems transparent, fair, and easy to interpret.

You'll also analyse how large language models (LLMs) are built and explore ways to make AI behaviour more understandable and responsible.

This module will give you a practical introduction to reinforcement learning and how it helps agents make decisions in changing environments.

You’ll look at the main algorithms behind reinforcement learning and try out ways of training agents to solve different kinds of problems, both in simulations and simple real‑world scenarios. You’ll also explore how agents are designed, how they choose actions, and what makes some learning strategies more successful than others.

This module gives you the space to demonstrate what you can achieve when you take full ownership of a significant piece of work, from identifying a topic to presenting your final outcomes.

You’ll begin by defining a clear problem or question and examining its feasibility. This involves exploring the wider context, understanding the needs and expectations of relevant stakeholders and clarifying the objectives of your proposed project. You’ll develop a plan that sets out the methods you’ll use, supported by a careful consideration of ethical responsibilities.

Through guided workshops, you’ll explore each stage of the project process, looking at research design, data collection, analysis and project organisation.

You'll also explore how appropriate technologies and research methods can support your investigation. You'll work with a supervisor to provide you with specialist guidance as you refine your approach and move towards producing your final output.

Your project may take the form of a written investigation, practical solution, digital artefact, or another approved format, but all projects must include evidence‑based conclusions or recommendations.

Alongside this hands-on element, you'll present your findings to different audiences and explain their significance or potential impact. These reflections will enhance your communication skills to support your future professional ambitions.