MSc Artificial Intelligence

This module introduces you to the study of complex networks and the methods used to analyse and optimise them. Complex networks appear in many real-world settings, including social media, transport systems, biological systems, communication infrastructures, and organisational networks. By examining how entities are connected and how information, influence, or resources move through these connections, network analysis helps us understand the structure, behaviour, and performance of complex systems. Throughout the module, you will explore key concepts and techniques for analysing network structure, identifying important nodes and relationships, and using network-based evidence to support decision making and optimisation. You will also consider how methods from data science and artificial intelligence can be used to improve information exchange and performance within complex networks. A practical focus is maintained throughout the module through the use of SAS Viya, which you will use to visualise, analyse, and interpret network data from a range of application areas. By the end of the module, you will have developed both conceptual understanding and practical skills in working with complex networks and communicating network-based insights effectively.

This module introduces you to the core principles and practices of Data Science using the R programming language. The module focuses on the practical skills needed to work effectively with data, from importing and tidying data through to transforming, visualising, modelling, and communicating results. You will learn how to use a modern data science workflow to explore datasets, identify patterns, generate insights, and present findings clearly. The module emphasises hands-on work with data and develops your confidence in using R and its supporting tools to solve realistic data problems. Alongside technical skills, you will also consider issues such as reproducibility, clarity of communication, and the importance of well-structured analytical workflows. By the end of the module, you will have developed a practical understanding of key data science techniques and the ability to apply them in a structured and professional way. The module provides an important foundation for further study in analytics, visualisation, machine learning, and applied data science projects.

This module introduces you to the principles, architectures, and applications of deep machine learning, with a focus on how modern neural networks can model complex, high-dimensional data. Throughout the module, you will explore key concepts such as neural network design, backpropagation, optimisation strategies, regularisation techniques, and model evaluation. You will study a range of architectures, including feedforward neural networks, convolutional neural networks, recurrent neural networks, and transformer-based models, and consider their strengths and limitations across different domains. A strong practical emphasis is maintained through hands-on implementation and experimentation using industry-standard tools such as Python and deep learning frameworks (e.g., TensorFlow or PyTorch). You will work with real-world datasets to design, train, and evaluate models, gaining experience in debugging, tuning, and improving performance. Consideration is also given to the ethical and societal implications of deep learning, including issues of bias, interpretability, and responsible deployment. By the end of the module, you will have developed both a rigorous conceptual understanding of deep machine learning and the practical skills needed to design, implement, and critically evaluate deep learning solutions in a variety of applied contexts.

This module helps you develop as a responsible, reflective and employment ready computing professional. It has been designed to support your transition from academic study into professional practice by combining digital citizenship, professional skills and authentic work related experience. Through this module, you will explore what it means to act as a good digital citizen in contemporary computing and data driven environments. You will consider the ethical, legal and professional issues that shape practice in areas such as artificial intelligence, data use, privacy, GDPR, digital regulation, responsible innovation and professional conduct. You will examine how computing professionals make decisions that are not only technically effective, but also ethical, inclusive and socially responsible. Alongside this, the module develops the practical skills needed to succeed in the workplace. You will strengthen your communication, teamwork, reflective practice, professional presentation and career planning skills, while learning how to present yourself effectively in professional settings. A key feature of the module is the opportunity to engage in authentic work experience through flexible placements, micro-internships, live briefs or related professional activities. These experiences are designed to help you apply your learning in real-world contexts, build confidence and develop insight into the expectations of employers and the wider profession. By the end of the module, you will have a stronger understanding of your professional identity, your responsibilities as a computing practitioner, and the standards expected in modern digital workplaces. The module will help you connect your academic learning with career development and prepare you to contribute positively, ethically and professionally in a wide range of computing related roles.

This module introduces you to the fast moving field of Generative AI and helps you develop the knowledge and practical skills needed to use it critically, creatively, and responsibly. You will explore how systems such as large language models work, where they are useful, and where their limitations lie. The module gives you a clear grounding in key ideas such as tokens, embeddings, transformers, model training, fine tuning, and alignment, but in a way that focuses on understanding and application. As the module progresses, you will learn how to design effective prompts, evaluate AI outputs, and think carefully about how people interact with conversational AI systems. You will also explore techniques such as retrieval augmented generation (RAG), model adaptation, and agent based workflows, helping you understand how generative AI can be connected to tools, data, and real world tasks. Alongside the technical content, the module places strong emphasis on responsible and professional use of Generative AI. You will examine issues such as bias, misinformation, privacy, intellectual property, security risks, and safe system design, so that you can make informed judgements about when and how these technologies should be used. The module is practical, applied, and forward looking. It is designed to help you build confidence in working with Generative AI in academic, professional, and research contexts, while also encouraging you to question claims, test ideas, and engage with emerging developments in this rapidly changing area. By the end of the module, you will be able to understand core Generative AI concepts, experiment with key techniques, and critically evaluate the opportunities and risks of using these technologies in practice.

This module provides you with the opportunity to undertake a substantial independent project in an area of Artificial Intelligence that reflects your academic interests, career goals, and specialist knowledge developed during your course. It is designed as a major integrative exercise in which you will apply and extend the knowledge, skills, and techniques gained from earlier modules to investigate a complex problem in depth. Working under the guidance of an academic supervisor, you will identify, plan, and carry out a project that may involve research, analysis, design, development, implementation, and evaluation within an Artificial Intelligence context. Your project may focus on areas such as AI analysis, machine learning, predictive modelling, AI engineering, responsible data use, or the application of AI methods to a real-world problem. The module requires you to demonstrate independence, critical thinking, and professional judgement as you define a project, select appropriate methods, manage your progress, and evaluate your outcomes. You will be expected to engage with relevant literature, justify your methodological choices, and produce a substantial artefact supported by rigorous analysis and reflection. Through a combination of workshops, independent learning and supervisory meetings, the module supports you in developing your project from initial idea to final submission. By the end of the module, you will have completed a significant piece of work that demonstrates your ability to undertake advanced study or professional practice in Artificial Intelligence.

This module introduces you to the core concepts, methods, and applications that underpin Artificial Intelligence. It explores how intelligent systems are designed to perceive information, represent knowledge, reason about problems, learn from data, and make decisions in complex environments. The module provides a foundation for understanding both classical and contemporary approaches to AI, including search, optimisation, knowledge representation, reasoning, uncertainty, and machine learning. You will examine the principles that allow AI systems to solve problems, support decision making, and operate in real-world contexts. Alongside the technical foundations, you will also consider the strengths, limitations, and wider implications of Artificial Intelligence, including issues of ethics, responsibility, and professional practice. By the end of the module, you will have developed a broad understanding of the key principles of AI and how they are applied across a range of domains. This module provides an important foundation for further study in specialist areas such as machine learning, data science, robotics, intelligent systems, and generative AI.

This module prepares you to design, plan, and justify a substantial Masters level project in a computing related discipline. It introduces the principles of research design, academic and professional enquiry, and project planning in areas such as Computer Science, Data Science, Artificial Intelligence, and Cyber Security. You will learn how to identify and refine a suitable research or development problem, review and evaluate relevant literature, formulate research questions or project aims, and select appropriate methods for investigation. The module will also help you develop the professional skills needed to manage a project effectively, including planning, risk awareness, ethical consideration, communication, and reflective practice. Alongside research methods, the module explores professionalism in computing, including ethical and responsible practice, legal and regulatory awareness, data and AI ethics, cyber responsibility, and professional standards expected in industry and research environments. You will consider how computing professionals design and conduct work in ways that are rigorous, responsible, and aligned with the needs of stakeholders. The assessment for the module is a research proposal in which you will define and justify a project idea, demonstrate awareness of relevant literature and methods, and present a realistic project plan. This will help prepare you for your final MSc project and for professional practice in technical and research led roles.