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 problems more “intelligently”, and ultimately to build intelligent artefacts.

Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are many tools used in AI, including mathematical optimisation, 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. 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. The emphasis will be 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”.

This module aims to give you the guidelines needed to analyse, evaluate and implement a responsible AI solution which considers ethical, legal and societal aspects of AI. 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 the current framework in place for the development of ethical AI solutions and will be asked to apply those principles. You will be specialised in the societal, legal and ethical impact of AI and learn the skills required to design these systems.

The module content is divided into 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.

There are three main types of study:

• A practical software development study, which aims to explore an area or ideas, or demonstrate a concept through appropriate software development, testing and critical analysis.
• A research-focused study, which aims to draw on  practical data to assess critically a specified area or topic. The empirical data may come from a study undertaken by the student with an appropriate organisation (agreed by the School of Computing and Engineering or a metaanalysis of data already available in published relevant literature.
• An Information Systems case-study approach which aims to draw on one or more specific case studies from an IS organization in order to explore an idea / concept. The case study provides the basic data for the research.

Regardless of the study pursued, it will be necessary to undertake a literature review because both the ability to develop knowledge and skills through self-guided learning and the ability to critically appraise literature from the perspective of the problem being addressed are fundamental at master’s level.

• To raise awareness of the nature of the contemporary employment environment
• To provide a structured framework to consider the challenges of gaining employment and
  developing occupational and professional skills
• To identify and develop key assets for employability and how to manage them
• To develop key transferable competences
• To prepare to enter and engage with the employment market and to exploit personal assets and
  employability.