Start Date between May 26 - July 26

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 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.