Artificial Intelligence BSc Honours

This module provides an introduction to interactive and generative AI systems. You'll develop the skills to optimise the responses of generative AI and apply interactive AI systems to real-world problems.

This module provides a comprehensive introduction to data modelling and database architecture. It covers foundational concepts such as relational database modelling and the use of Entity-Relationship (ER) diagrams for conceptual and logical database design. You'll gain hands-on experience with Structured Query Language (SQL), learning to perform key operations, including creating, reading, updating, and deleting (CRUD) data within a database. Through a combination of lectures and practical computing sessions, you'll become proficient in using standard database management systems and developing database-driven applications with practical programming examples. Additionally, the module explores contemporary Big Data technologies, introducing you to NoSQL databases and their applications in handling complex, large-scale data sets.

This module provides an introduction to the core concepts and techniques of artificial intelligence, from search algorithms, knowledge representation, and machine learning paradigms, to the wider implications for AI in contemporary society. Students will gain a foundational understanding of AI's history, its evolution, and its implications for various industries.

This module explores the principles and practices of human-centred and ethical AI, focusing on responsible AI development and deployment. It begins with an introduction to ethical AI, covering key ethical frameworks that guide decision-making. Topics such as bias, fairness, and inclusivity are examined to ensure AI systems are transparent and equitable. The module also delves into legal and regulatory considerations, including GDPR, privacy, data protection, intellectual property, and copyright. Real-world AI case studies provide practical insights, while UX design principles emphasise accessibility and inclusivity. Finally, students will learn how to design ethical AI systems and explore emerging trends shaping the future of responsible AI development.

This module provides an introduction to the mathematical concepts that underpin artificial intelligence, including algebraic, analytical, logical, and statistical techniques. Students will gain a foundational understanding of mathematical processes pertinent to artificial intelligence, including the analysis of algorithms and datasets.

An introduction to computer programming in a high-level programming language. The module concentrates on teaching the fundamentals of programming and algorithm design. Basic coding structures such as sequence, selection, and iteration will be covered. There will be an emphasis on practical exercises to develop programming experience and confidence.

This module provides a foundation in deep learning, starting with tensor operations and neural network fundamentals using TensorFlow. Students learn to implement, train and evaluate neural networks, progressing from basic operations to advanced architectures. The module covers the full deep learning development cycle from data preparation through to model deployment, with particular focus on sequence modelling tasks including time series and natural language processing. Practical implementation is emphasised throughout, using industry-standard frameworks and best practices.

This module is designed to equip you with the necessary skills to successfully undertake industrial projects in the IT and Data Science fields. It will develop the ability to apply project management and systems thinking methodologies to support the design and delivery of technical solutions in different global contexts. You'll be introduced to a range of contemporary development, testing and deployment strategies. You will gain valuable practical and professional experience working as part of a team on a prototype solution on behalf of an external organisation.

This module provides an introduction to the core concepts and techniques of game theory and reinforcement learning used to inform rational decision making. Students will develop practical programming skills and gain a foundational understanding of the underlying algorithms.

This module provides an understanding and hands-on experience in the fields of machine learning and data mining, covering the full life-cycle from preparing data to validating and optimising the learned model. The module covers different algorithms and approaches to machine learning and data mining, and the issues of using them on data sets of different sizes and complexity.

This module introduces the fundamentals of robotics and focuses on selected topics pertaining to this discipline. Its introductory part overviews the nature of robotics and, related to it, challenges and issues. System modelling introduces techniques of deriving and computer implementation of models of dynamic systems with a special focus on kinematics of robots. Fundamentals of control cover the structure, basic analyses and real-time implementations of control systems.

This module provides an applied understanding and practical experience of the data engineering pipeline to gather, understand, combine, clean, process and store data for further analysis. The module explores data pre-processing strategies and focus on both structured and unstructured data. Furthermore, the module covers issues related to data quality and governance, and metadata management.

This module enables you to create engaging data visualisations to effectively communicate the results of data analysis to a diverse audience. You'll learn how to encode information in visual form and will create infographics and dashboards. You'll also learn to use the power of storytelling to create engaging data narratives. We use a mixture of open source tools, such as R and ggplot2 and commercial tools, like Microsoft Power BI or similar.

This module introduces you to modern game engine architecture and technologies. The conceptual architecture framework and the subsystem integration including the low-level foundation systems, the rendering engine, game asset management, the physics simulation, event-based gameplay system will be critically accessed. You'll gain the theory underlying the various subsystems that comprise a commercial game engine and the data structures and essential algorithms and develop practical skills that are typically used to implement a 3D game prototype using industry game engines.

This module covers key topics in deep learning, including computer vision fundamentals, advanced architectures like Transformers and Graph Neural Networks, and generative models for Large Language Models. It also explores deep reinforcement learning, explainable AI, model uncertainty, and domain adaptation. The module concludes with discussions on the latest advancements in deep learning research.

This module provides practical knowledge of applying Large Language Models (LLMs) and Generative Artificial Intelligence (Gen AI) in real-world business applications, along with techniques and strategies for training, tuning, and deployment. It provides the students with a technical foundation to help them bridge the gap between their taught knowledge in LLM and its counterparts' deployment in marketing, sales, finance, product, and more.

Students will learn the application of LLMs to transform business solutions while ensuring their security, resiliency, ease of management, and integration with existing technology ecosystems.

This module requires students to combine previously acquired knowledge and techniques, with new knowledge/ideas gained from investigation and research, and produce an extended piece of work related to Artificial Intelligence. It involves the conceptualisation, design, implementation and evaluation of a substantial piece of software, process, model or experimental study.

This module provides an introduction to the cloud computing principles and practices essential for secure and scalable AI deployment. It covers foundational topics such as cloud computing fundamentals, AI deployment workflows, and the differences between on-premise and cloud-based solutions. Students will also explore key considerations in security, compliance, and governance, along with practical aspects of real-time versus batch inference, CI/CD, and model orchestration. Additionally, the module addresses model performance tracking, experiment management, and A/B testing, ensuring a comprehensive understanding of best practices in maintaining data quality, handling data drift, and achieving model reproducibility and versioning.

This module builds on the knowledge and skills developed in the Level 5 module Robotic Principles. You'll focus on robotic dynamics and the design of robotic control systems, preparing you to undertake complex tasks involving the analysis and synthesis of robotic systems. Ethical and societal issues related to robotics, introduced at Level 5, are explored in greater depth and breadth, encouraging critical reflection on the wider impacts of robotic technologies.

This module provides an in-depth analysis of the state-of-the-art in Big Data Analytics. In addition to providing an overview of key concepts and technological trends, the module explores how Big Data systems are implemented and utilised in the business context to derive important insight and support real-world decision making. Furthermore, the module will cover key technical challenges to Big Data Analytics, including issues relating to data governance and data quality.

Apply prompt engineering to build business generative AI models. This module is designed to equip you with the knowledge and skills needed to effectively integrate AI and generative AI into your business operations, processes and strategies. You will explore AI not only as an automation tool, but as a strategic enabler that transforms business operations, enhances innovation capabilities, and advances your career in an AI-business driven world.

Creating robust game world environments and model individual components using logic and mathematical modelling to replicate human behaviour is one of the greatest challenges for game developers, yet the commercial success of a game is often dependent on the quality of its intelligent modelling. This module introduces an engagingly realistic and immersive experience in modern videogames for creating game terrains and simulating complex behaviour of game objects. The main focus of the module is to learn how to create an automatic random game world with metrics to generate human-like intelligent behaviours primarily in non-player characters (NPCs). The algorithms and logic covered in this module are also widely used in a variety of computing sectors which will make the module a great choice for CS students.