MA Artificial Intelligence

This introductory module explores human-centred AI, focusing on the ethical, social, and professional implications of AI technologies. You’ll explore how to design AI systems that enhance human capabilities while promoting inclusivity and minimising harm. You'll also learn about how artificial intelligence can be applied in various contexts, and critically engage with current research. Topics include AI in daily life, human-AI interaction, ethics, and bias. This will prepare you to navigate AI's societal impact and professional relevance with a responsible approach.

This module teaches the statistical learning and machine learning methods that form the foundation of modern AI. Although these methods rely on advanced mathematics, you don’t need a background in maths or computer science to complete this module. You’ll learn about key AI algorithms, explore model development, and study the hardware and software they require. The module also covers data types, learning methods, and how to balance accuracy with interpretability. Topics include regression, classification, neural networks, and more.

In this module, you'll explore essential techniques and practices for employing AI in various industry settings. You’ll also learn to develop, deploy, and manage AI systems effectively. We'll focus on real-world applications, providing insights into the computational and data aspects of AI, basic programming skills, and the use of digital tools in the AI environment. This prepares you to create and monitor effective AI solutions.

This module explores how AI can be harnessed to address social issues and shape public policy. You'll learn about how AI tools enhance our understanding and responses to societal challenges, and impact policymaking. From a critical standpoint, you’ll examine AI’s applications, ethical implications, bias mitigation, and data protection. Through case studies and theoretical learning, you’ll design AI-driven strategies for complex social problems. You’ll be capable of contributing meaningfully to policy discussions and influencing them as a result.

This module equips you with advanced knowledge of the legal, societal, and ethical challenges posed by AI technologies. It covers interdisciplinary and global perspectives on embedding fairness, transparency, privacy, and inclusiveness in AI applications across sectors like healthcare, education, and criminal justice. It also explores the evolution of AI principles, AI-driven privacy issues, and the impact of AI regulations on digital inclusion and democracy. You’ll learn to assess AI challenges and regulation frameworks from a critical standpoint.

This module provides an in-depth examination of generative AI, focusing on technologies such as transformers and large language models. It includes practical training in prompt engineering, model fine-tuning, and deployment. You’ll also address the ethical implications, societal impacts, and potential risks of generative AI, including its contribution to discussions on Artificial General Intelligence (AGI). You’ll engage with both the technical aspects and the broader concerns associated with these technologies.

This module equips you with the essential skills required to carry out research and pursue lines of enquiry. You’ll be guided through every aspect of the research process, from formulating your research topic to selecting appropriate research methods. With an emphasis on professional practice and critical reflection, this module teaches you to tackle ethical considerations, manage data securely, and prepare research proposals. This knowledge will be crucial for your dissertation, and future research projects.

This module demystifies machine learning by exploring a broad range of techniques and their real-world applications. You’ll learn to distinguish between statistical modeling and machine learning approaches, assess machine learning properties for specific tasks, and understand the uncertainty inherent in machine learning outputs. We’ll cover predictive modeling, ensemble methods, representation learning, and data organisation. You’ll also learn how to make outputs interpretable and accessible. By the end, you’ll be able to communicate machine learning concepts effectively to non-experts.

This module equips you with an understanding of AI's capabilities and its limitations through in-depth exploration of machine learning algorithms like Random Forests and neural networks. You’ll assess real-world AI applications, interpret AI model results, and explore ethical considerations surrounding data use. You’ll learn to select and evaluate AI tools for specific tasks and develop an awareness of the practical and ethical dimensions of data science.

In this module, you'll independently design and conduct a research study on a topic of your choice related to the course. This involves identifying and evaluating relevant literature, formulating a research question, and employing appropriate methods for data collection and analysis. You’ll need to critically assess and reflect upon your research process and manage methodological challenges. You’ll then present your findings in a structured, well-cited report. You’ll receive guidance on this from your dissertation supervisor.