Intelligent Systems and Machine Learning MSc

Core modules

• DSP Applications and ARM® Technology

  DSP is an integral part of electronics system design and ARM is a major player in the design and manufacture of microelectronic components. Our module uses ARM hardware, software and development tools and covers topics such as: algorithms, fixed gained and adaptive filters, spectral analysis, application, ARM Processors, Cortex Processors, ARM development tools.

• 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.

• Remote Sensing and the Internet of Things

  The internet allows devices, systems and services to interconnect and provide cost effective and novel applications and services in almost all fields. Connected objects are identified uniquely by their IP addresses and may be sensors (e.g. medical sensors, gas and electricity meters, temperature/pressure/light sensors) or actuators (household appliances, motors, bulbs, locks, alarms) which communicate with each other via the Internet of Things (IoT). IoT uses sensing technologies to collect data from objects, employs technologies like artificial intelligence and cloud computing to store and analyse the data collected from the sensors, and eventually uses remote control technology to control the objects. This results in the creation of smart networks that make services more efficient and adaptive and all leading to an improved working and living environment. You will learn about the design and development of IoT systems, including the layered architecture of IoT, technologies on each layer, and applications of IoT in every corner of life. You will develop your hands-on skills by demonstrating, experimenting, and implementing testbeds in the lab. Our lectures will introduce the architecture and various technologies for IoT and your lab work will allow you to design and implement technologies.

• 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.