Artificial Intelligence and Big Data MSc

Core modules

• Semantic Data Technologies

  Businesses, large organisations and government departments at a local and European level are increasingly producing and using large semi structured data generated from data collection from their own activities and from the wider internet and social media. Semantic Data Technologies both identify and interpret the meaning of data according to its context. This module introduces this concept, alongside the key technologies and techniques for storing data and develops the skills needed for sophisticated data management. The technologies supporting the 'semantic web' have provided the tools, methodologies and theoretical underpinnings to enable data to be automatically interpreted by machines for knowledge based tasks. These techniques are increasingly being used in a more general approach to handling the kind of non-structured data that is important for recording, evaluating and guiding policy and decision making processes. This module will provide the knowledge and skills for students to structure semantic data, develop ontological models and use these to create knowledge based applications to analyse data, support decision making, enable intelligent access to information and add value to data. After completing this course students will be able to design and implement applications that comply with data re-use standards, utilise the semantic web as well as applying those technologies to the organisation and analysis of big data. The knowledge and skills learned in this module complement those of information system analysis design and data base implementation as well as advanced web server and application development, providing a theoretical and practical base for enterprise wide data handling.

• Advanced Machine Learning

  Machine learning is a sub-discipline of the Artificial Intelligence that deals with teaching the computer to act without being programmed. In this module you will learn about the tools and algorithms that can be used to create machine learning models. Big data and their economic, legal and ethical aspects are explored, along with data acquisition and pre-processing methods that are used to make these suitable for machine learning algorithms. You will also look into how large data sets should be divided into a training set and a test set and different types of problems that can be solved with machine learning will also be introduced. A range of parametric algorithms such as linear regression, logistic regression, and non-parametric algorithms such as K-Nearest neighbour, decision trees, SVMs, will be discussed. To be able to evaluate a model, a few performance metrics will be explored, the metrics chosen influence how the performance of machine learning algorithms are measured and compared. An important concept that you have to be aware of when training machine learning algorithms is ‘overfitting’, an over fitted model will have a low accuracy and therefore you will learn how to use regularization to avoid overfitting.

• Research Methods

  Gain support and foundations in the research skills needed for your Masters level dissertation. You’ll investigate research activities including project management, research project design and analyses, ethical considerations and dissertation preparation.

• Applications of Machine Learning

  This module builds on, and extends the Advanced Machine Learning module by looking at two main applications of machine learning – image recognition and natural language processing. You will study various algorithms for image recognition and will do a variety of experiments, including hand writing recognition, face recognition, medical picture analysis and speed detection. You will explore different machine learning models that can be used in number of natural language processing tasks such as tokenization, named entity recognition, and classification. You will also investigate and experiment with the models and algorithms learned during practical sessions.

• Neural Computing and Deep Learning

  Deep learning and neural networks have revolutionised numerous fields in recent years. From smartphones and smart watches to cars and even house appliances, electronic devices are increasingly making use of machine learning and neural computing to take decisions, categorise and classify items, learn behaviours, assist us with choices and make prediction. The near future will see an even larger number of “self-learning” devices in almost every aspect of our lives. This module explores two main areas of Intelligent Systems: neural networks and deep learning. You will start analysing the structure of neural networks, from the theoretical aspects to the practical implementations, both biological and artificial. You will then move to the concept of supervised and unsupervised learning and analyse some of the most widely used deep learning methodologies. You will cover some of the main models and algorithms for regression, classification, clustering and decision making processes. The module will include applications of neural computing and deep learning to big data in physical and biological sciences, finance and social sciences. You will use primarily the Python programming language and requires familiarity with basic linear algebra, probability theory, and programming in Python.

• Major Project

  This module supports students in the preparation and submission of a Master's stage project, dissertation or artefact. The Module provides the opportunity for students to select and explore in-depth, a topic that is of interest and relevant to their course in which they can develop a significant level of expertise. It enables students to: demonstrate their ability to generate significant and meaningful questions in relation to their specialism; undertake independent research using appropriate, recognised methods based on current theoretical research knowledge, critically understand method and its relationship to knowledge; develop a critical understanding of current knowledge in relation to the chosen subject and to critically analyse and evaluate information and data, which may be complex or contradictory, and draw meaningful and justifiable conclusions; develop the capability to expand or redefine existing knowledge, to develop new approaches to changing situations and/or develop new approaches to changing situations and contribute to the development of best practice; demonstrate an awareness of and to develop solutions to ethical dilemmas likely to arise in their research or professional practice; communicate these processes in a clear and elegant fashion; evaluate their work from the perspective of an autonomous reflective learner.