Artificial Intelligence MSc

Compulsory

• Quantitative Data Analysis
  The aim of this module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. Content covers a practical understanding of core statistical methods in data science application and research, such as bivariate and multivariate methods, regression and graphical models. A focus is also placed on learning to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.
• Data Visualisation
  The aim of this module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets to, for example, decision makers. Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design. The role of interactivity within the visualisation process will be explored and an emphasis placed on visual storytelling and narrative development.
• Research Project Management
  This module aims to develop and deploy the skills necessary to design a scholarly piece of research work to address an identified problem area within the chosen field of study.
• Ethics and Governance of Digital Systems
  This module aims to develop a critical understanding of topics related to the handling and governance of digital information in contemporary systems contexts. Such topics will include the way that networked and intelligent systems are designed and used; the motivations for their adoption; the substantive issues arising; and approaches to their regulation and governance. Examples from the public and private sectors will be used to illustrate these developments.
• Artificial Intelligence
  The aim of this module is to introduce the key concepts, principles and fundamental methods of artificial intelligence, and to develop your skill in analysing of problem requirements, applying appropriate artificial intelligence methods to defined problems, and evaluating the effectiveness of the adopted approach.
• Deep Learning

  Within this module, an in-depth introduction will be provided to the area of learning using deep neural networks. A wide variety of the architectures of deep neural networks and their learning methods will be covered, including convolutional networks, recurrent networks, generative models and deep reinforcement learning etc. The main focus of the module is to develop students’ skill in analysing of problem requirements, applying appropriate deep learning methods to real-world problems, and evaluating the effectiveness of the adopted approach.

• Modern Data
  The aim of this module is to provide an introduction to data management and exploration. An overview of current industry standard processes to modern data analysis will be presented, and you will learn to design and plan a predictive analytics project. Basic concepts of data management and retrieval will be discussed. Well established strategies and approaches to data understanding, data preparation and cleaning will be presented.
• Machine Learning
  The aim of this module is to develop the reflective and practical understanding necessary to extract value and insight from heterogeneous data sets using statistical learning. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the processing of heterogeneous data. Content will cover machine learning techniques, such as principal component analysis, cluster analysis, decision trees and random forest, support vector machines, as well as approaches to performance evaluation.