Artificial Intelligence MSc

This module will introduce you to the fundamentals of AI with a focus on

1. the structures, resources and processes that together make up an intelligent agent
2. the techniques, models and tools that can be used to simulate the “intelligent” processes
3. the skills and capabilities necessary to critically review AI literature and/or products, to synthesise ideas, to systematically solve AI problems and to communicate effectively.

You will gain the knowledge and skills required to understand the fundamentals of AI, to solve real world problem more “intelligently”, and ultimately to build intelligent artefacts.

Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are a large number of tools used in AI, including mathematical optimization, logic, probability, and many others. AI has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.

Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.

This module familiarises you with some basic machine learning algorithms and techniques and their applications, as well as general questions related to analysing and handling large data sets. Several software libraries and data sets publicly available will be used to illustrate the application of these algorithms. The emphasis will be thus on machine learning algorithms and applications, with some broad explanation of the underlying principles.

This module provides an introduction to deep learning. You will learn the fundamentals, models, algorithms, implementation and recent progress of deep learning, and obtain hands-on experience on training neural networks.

You will start with exploring artificial neural networks (ANNs) and its operation, gradually moving onto exploring the approaches to develop large ANNs with many layers, also called deep neural networks (DNNs).

This is followed by the understanding of how deep learning works and approaches to train the networks covering the technical know-how such as vanishing/exploding gradient problems, activation function and training optimiser, which is often known as “deep learning”.

The module will enable you to focus on current ethical issues in AI and undertake an investigation into how to resolve them in future. You will learn current framework in place for development of ethical AI solutions and will be asked to apply those principles.

You will be able to specialise in societal, legal and ethical impact of AI and learn skills required to design these systems.

The module content is divided to three parts:

• Responsible AI in Design – to ensure full awareness of consequences for people by development choices
• Responsible AI by Design – to understand the behaviour of AI systems and the integration of ethical reasoning as part of the algorithms
• Responsible AI for Designers – to learn the codes of conduct and the current standards that ensure the integrity of developers.

This module will ensure that you are fully prepared to undertake applied research at master’s level. You will be able to pursue your research ideas and back them up with appropriate data and statistics.

The assessment of the module will prepare you for the delivery of a dissertation proposal. The topics covered in the module include:

• introduction to research
• research process and developing research proposal
• developing research objectives, choosing research methods, presenting & analysing data, and making
  conclusions
• building a literature review
• research methodologies in computing
• research ethics
• writing dissertation proposal
• writing dissertation, managing references and using document tools
• identification and use of subject related library resources; Understanding plagiarism
• developing a career plan.

Big data is a fast-growing field and skills in the area are some of the most in-demand today. Big data technologies cover a range of architectures, frameworks and algorithms designed to handle very large and often highly complex datasets.

The module will enable you to understand big data, its applications and associated issues for storing, managing, processing and analysing massive amounts of datasets, as well as become familiar with software tools and frameworks underpinning big data analytics.

You will also acquire the knowledge of statistical, mathematical and machine-learning techniques, and develop the ability to design and implement big data analytics modelling and applications to real-world problems.

This module will explore the advanced principles and techniques currently being used in real-world computer vision systems, and the research and development of new systems.

Computer vision lets computers gain high-level understanding from digital images or videos, and seeks to automate tasks that the human visual system can do. It has become ubiquitous with applications in search, image understanding, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, segmentation, localisation and detection.

In this module you will learn how digital images are formed, how they are represented and stored on computers, and how they can be processed by computers to extract semantic information. You will have the opportunity to develop algorithms for detecting interesting features in images, design convolutional neural networks to perform tasks such as image classification, and explore techniques for solving real-world problems such as object detection. Relevant programming language (e.g. Python, OpenCV, MATLAB, etc.) will be used for model developments.