Artificial Intelligence - MSc

Computing proficiency is essential in modern data science and a firm grounding is required before the more technical themes of Artificial Intelligence (AI) can be studied. This is the introductory module for the programme where no prior computational foundations are assumed. A holistic view of computing will be provided covering the important themes of data management, programming and security considerations. The module will start from principles of data structures and algorithms, using Python for implementations, which will then be applied to database structures, both SQL and NoSQL, enabling learners to construct programs using standard industrial practices.

Artificial Intelligence (AI) is a core component of computer science, aiming at developing intelligent agents that mimic human’s cognitive capability in learning, reasoning, and problem-solving. As a branch of AI, machine learning (ML) allows to learn and adapt (from examples), rather than being explicitly programmed for a particular outcome. This module will offer you the statistical grounding required by data scientists to construct and evaluate the effectiveness of AI systems. Probability theory and statistical evaluation will lay the foundations to be used in a range of real-world scenarios. Standard machine learning techniques for regression, clustering and classification will be covered in this module. You will develop the practical skills needed for applied machine learning including the use of existing tools and techniques needed to solve industry-standard problems based on use cases extracted from real domains.  

Modern artificial intelligence (AI) has become popular primarily because it can deal with data of various kinds. The ability to handle this data arises thanks either to the pre-processing that makes the data amenable for subsequent analysis/modelling, and/or to the nature of AI algorithm itself. Over the past half-century a range of different algorithms and approaches have been designed to handle structured data challenges in areas such as time series prediction, natural language modelling and image analysis. In this module you will be taught the targeted ways in which inferences can be made in these domains motivated with real-world projects. In addition to this you will be given a grounding in optimisation of parametric models and subsequent feature selection will be given to identify optimal tools for data analysis. 

Following a series of breakthrough developments starting in 2006 the field of Deep Learning has attracted interest from a range of fields and is now receiving billions of pounds of investment across the world. Harnessing these techniques can leverage insight into problems not previously recognised. This module will contrast the application domain-specific methods from the Applied AI module with a set of generalised Deep Learning machines to show how they are applied to unstructured data. In particular, the module will teach you how to attack image analysis, natural language and reinforcement learning problems with these models whilst critically analysing the costs and benefits of implementation.  

The purpose of the module is to enable you to undertake a sustained, in-depth and research-informed project exploring an area that is of personal interest to you. In agreement with your supervisor, you will decide upon your topic which will take the form of a practical outcome (artefact) with accompanying contextual material. This can be oriented towards any area of AI and there is the opportunity for an industrial project run in co-operation with one of our partner organisations.

The aim of this module is to identify, apply and design data visualisations. The module provides you with the fundamental principles and practice-based activities needed to design data visualisations for both different contexts, and different types of data. More advanced visualisation concepts and tools for analysing multi-dimensional data and large data sets will also be examined and appraised. You will learn how to employ visualisation as a tool that can help users understand large and complex data sets, conveying the outputs to a diverse audience.

Artificial Intelligence (AI) is expected to transform the way we humans live and work helping us to solve complex problems and live more enriched lives. From healthcare to finance, AI now impacts nearly every industry through dramatically improving the efficiencies of workplaces and expanding the capabilities of the work humans can do. When AI takes over repetitive or dangerous tasks, minimises occurrences of ‘human error’ or support decision making, it frees up the human workforce to do work they are better equipped for; tasks that involve creativity and empathy among others. This module will discuss the impact of AI with focus on AI project life cycles, horizon scanning, AI ethics and AI applications.

This module considers financial risk management strategies; how risk is evaluated; the methods that are available to hedge against risk and the strategies that are employed to mitigate risk. This is complemented by consideration of examining the impact of financial regulation on emerging FinTech businesses and considers the ways in which new technology challenges traditional models of financial regulation and ethical dilemmas.

The purpose of this module is to help you to develop a critical understanding of the principles and practices of securities analysis and portfolio management, with a systematic focus on financial decision-making for investment. To achieve this goal, various models and methods will be applied to real-time financial market data for constructing efficient portfolios and identifying the optimal portfolio. By utilizing computational tools and professional databases, you can engage in effective learning and practical-based studies.