Start Date between May 26 - July 27

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.

This module introduces the principles and practices of machine learning operations (MLOps), focusing on how machine learning models are developed, deployed, and managed in production-oriented settings. It covers the end-to-end lifecycle of artificial intelligence (AI) systems, including workflow automation, version control, testing, and monitoring, alongside computational aspects such as GPU acceleration and high-performance computing for efficient training and experimentation. Students gain practical experience with tools and frameworks commonly used in production environments to build reproducible, scalable, and maintainable AI workflows.

This module is designed to help students develop and demonstrate the skills required to carry out a substantial individual project in a chosen aspect of Artificial Intelligence. Core skills for conducting the individual project are developed through guided online learning.

This module is designed to help students develop and demonstrate the skills required to carry out a substantial individual project in a chosen aspect of Artificial Intelligence. Core skills for conducting the individual project are developed through guided online learning.

This module aims to develop students’ ability to identify and evaluate the ethical, legal, and societal implications of artificial intelligence. Through a combination of ethical reasoning, case-based analysis, and applied discussion, students will explore concepts such as fairness, bias, transparency, and accountability. The module also aims to help students think intelligently and confidently about the ethical dimensions of AI and to view subsequent technical modules through a normative lens, fostering a reflective and responsible approach to AI practice. Learning activities are designed to encourage critical reflection, ethical reasoning, and interdisciplinary awareness, enabling students to critically evaluate real-world AI applications.

This module aims to develop students’ ability to identify and evaluate the ethical, legal, and societal implications of artificial intelligence. Through a combination of ethical reasoning, case-based analysis, and applied discussion, students will explore concepts such as fairness, bias, transparency, and accountability. The module also aims to help students think intelligently and confidently about the ethical dimensions of AI and to view subsequent technical modules through a normative lens, fostering a reflective and responsible approach to AI practice. Learning activities are designed to encourage critical reflection, ethical reasoning, and interdisciplinary awareness, enabling students to critically evaluate real-world AI applications.

This module aims to equip students with the mathematical understanding and intuition necessary to interpret, analyse, and design the methods that underpin modern AI. Building on key areas of linear algebra, vector calculus, probability, and analytical geometry, the module connects these concepts to the four foundational pillars of machine learning: classification, regression, density estimation, and dimensionality reduction. The module highlights how these mathematical principles give rise to practical modelling techniques that capture the process of learning from data and underpin approaches across the full spectrum of modern machine learning, from classical statistical models to artificial neural networks and generative AI. Learning activities combine explanatory notes, visual and geometric illustrations, worked examples, and guided problem-solving exercises to progressively develop both conceptual insight and analytical fluency.

This module aims to equip students with the mathematical understanding and intuition necessary to interpret, analyse, and design the methods that underpin modern AI. Building on key areas of linear algebra, vector calculus, probability, and analytical geometry, the module connects these concepts to the four foundational pillars of machine learning: classification, regression, density estimation, and dimensionality reduction. The module highlights how these mathematical principles give rise to practical modelling techniques that capture the process of learning from data and underpin approaches across the full spectrum of modern machine learning, from classical statistical models to artificial neural networks and generative AI. Learning activities combine explanatory notes, visual and geometric illustrations, worked examples, and guided problem-solving exercises to progressively develop both conceptual insight and analytical fluency.

This module introduces the fundamental principles and techniques of classical machine learning, with an emphasis on statistical approaches to learning from data. It explores how models capture patterns, make predictions, and generalise through both statistical and non-statistical methods, including regression, classification, clustering, and ensemble techniques. Students develop practical intuition and conceptual understanding of how different learning paradigms operate, preparing them to analyse and interpret model behaviour across diverse data-driven contexts.

This module introduces the fundamental principles and techniques of classical machine learning, with an emphasis on statistical approaches to learning from data. It explores how models capture patterns, make predictions, and generalise through both statistical and non-statistical methods, including regression, classification, clustering, and ensemble techniques. Students develop practical intuition and conceptual understanding of how different learning paradigms operate, preparing them to analyse and interpret model behaviour across diverse data-driven contexts.

This module introduces the principles and practices of machine learning operations (MLOps), focusing on how machine learning models are developed, deployed, and managed in production-oriented settings. It covers the end-to-end lifecycle of artificial intelligence (AI) systems, including workflow automation, version control, testing, and monitoring, alongside computational aspects such as GPU acceleration and high-performance computing for efficient training and experimentation. Students gain practical experience with tools and frameworks commonly used in production environments to build reproducible, scalable, and maintainable AI workflows.