| Module |
Credits |
Compulsory/optional |
|
Advanced Algorithms and Paradigms (COM)
|
30 Credits |
Compulsory |
| This module explores the extent to which different computational paradigms may be applied to problems in order to create appropriate solutions. To this end, this module will evaluate a range of different paradigms, such as the imperative, functional, concurrent and object-oriented paradigms and related computational algorithms.
A more detailed description of the module content is provided in the module delivery information for students. |
|
Responsible Technology (COM)
|
30 Credits |
Compulsory |
| This module is concerned with legal, social, ethical and professional issues that may affect the work of practitioners in the computing and technology sectors. Its main focus is on the ethical considerations inherent in the development of responsible technologies.
Topics covered are likely to vary from year to year to reflect contemporary research and issues. |
|
Advanced Computer Science Masters Project (COM)
|
60 Credits |
Compulsory |
| The project is a show piece opportunity for students to demonstrate what they know about current research and practices in computer science and show off their skills in applying their skills in a range of computer science topics in order to conduct a practical investigation of a particular computer science problem.
The project is a self-directed piece of work, conducted with minimum supervision that demonstrates the student's ability to plan and manage a substantial piece of work, and steer their own efforts.
Students are expected to be thorough in their work, and, particularly, identify and tackle any difficult or challenging aspects of the problems they are trying to solve. It is not just the quantity, or even the quality of work that is considered when grading the project, but the level of difficulty and the scope of the problem being addressed. |
|
Artificial Intelligence Programming (COM)
|
30 Credits |
Optional |
| This module will focus on the understanding and practical application of modern AI techniques. An indicative set of contents could be:
Working with the WEKA library, understand the details of decision-tree, clustering, Bayes and simple neural-network algorithms. Write programs which take user data (e.g. from a file or database) and develop models of that data, which are stored and then used at a later point for decision making. Discuss the risks and ethics of using human data, e.g. from facial recognition, to develop such models, looking at issues of bias, rights to privacy etc.
(Note that alternative module contents are possible. e.g. the module leader may instead focus on problem solving algorithms, aspects of robotics, or models of neural-network/human cognition with an introduction to deep learning, etc.) |
|
Big Data Analytics (COM)
|
15 Credits |
Optional |
| As part of this module, students will explore principles and applications of big data analytics. Indicative topic modules includes: types of big data; source of big data; big data mining; the analytic tools for big data management; big data applications; measurement; and standards and ethics in big data. |
|
Cyber Operations (COM)
|
15 Credits |
Optional |
| Today, Information Environments have become large, diverse and heterogeneous. System boundaries and jurisdictions have blurred, and system assets can be located in different geographical locations.
Stakeholders span across an integrated supply chain, new vulnerabilities are constantly being discovered and threat agents can expand their operations at an international level with little effort. The first and last line of defence to any information environment is a thorough, explicitly defined and well implemented cyber defence strategy.
The module will discuss the different cyber battle spaces and their domains of conflict looking in detail into the different disciplines of cyber operations and their functions under the context of protecting the corporate information environment. The module will touch upon the concepts of psychological operations, deception, network operations, situational awareness (including intelligence and counterintelligence) and of course information security and operational security. |
|
Data Mining (COM)
|
15 Credits |
Optional |
| In general, data mining techniques are used in organisations which collect data. This module will cover various data mining algorithms for discovering hidden knowledge, unexpected patterns, and new rules from data. Potential applications include market analysis and management (target marketing, customer relation management, market basket analysis, cross selling, market segmentation), risk analysis and management (forecasting, customer retention, quality control, competitive analysis), fraud detection and management, text mining (news group, email, documents) and web analysis, etc. |
|
Digital Forensics (COM)
|
15 Credits |
Optional |
| Digital forensics is the discipline that deals with the collection, examination, analysis and reporting of digital evidence. Digital forensics techniques are employed in various types of investigations from cybercrimes to corporate investigations. When a security incident is reported, digital forensics techniques are applied in order to retrieve related evidence in an evidentially sound manner. The constant development of a range of technologies is contributing to the establishment of formal methods and procedures for digital forensic investigators.
In this module the students should expect to familiarise themselves with the current research in the field and study the incident response and digital forensic investigation practices. They will study the formal methodologies, policies and legal guidelines and constraints involved in the investigation of a cyber security incident or cybercrime. A highly practical element should also be anticipated in this module as the students will work with various digital forensics tools in order to practically analyse digital devices and extract digital evidence. |
|
Distributed Systems Security (COM)
|
15 Credits |
Optional |
| A range of topics will be covered in this module. The detailed content will vary according to contemporary research directions. Case studies will be used throughout. Issues will be considered in relation to each topic as appropriate. These pervasive issues are: models, design, standards, protocols, and performance.
A more detailed description of the module content is provided in the module delivery information for students. |
|
Foundations of Data Science (COM)
|
30 Credits |
Optional |
| This module will include a study of a selection of topics centred around basic mathematical concepts and skills. For example: basic linear algebra, calculus, basic statistics, probability and fundamentals of Bayesian inference, basic set theory and information theory and instantiating some of the mathematical foundation as computational techniques.
Actual topics taught may vary from year to year. |
|
Information Security Management and Compliance (COM)
|
30 Credits |
Optional |
| Today, Information Environments have become large, diverse and heterogeneous. System boundaries and jurisdictions have blurred, and system assets can be located in different geographical locations.
Stakeholders span across an integrated supply chain; new vulnerabilities are being constantly discovered and threat agents can expand their operations at an international level with little effort. The first and last line of defence to any information environment is a thorough, explicitly defined and well implemented risk and threat assessment process.
The module will discuss procedures and controls for ensuring and assuring the information assets of an organisation. It will touch upon threat, risk, vulnerability and the approaches to incident and continuity management. It will look at security models, security standards and compliance issues for training students to conduct and manage risk and threat assessments. |
|
Machine Learning (COM)
|
30 Credits |
Optional |
| As part of this module, students will engage with a selection of research topics centered around machine learning including supervised and unsupervised learning, data mining, bio computation, evolutionary algorithms, data visualisation, and neural networks as models of brain function in health, disease and development.
Topics covered are likely to vary from year to year to reflect contemporary research. |
|
Measures and Models for Software Engineering (COM)
|
30 Credits |
Optional |
| In this module advanced issues of software engineering theory and practice are examined. The range of software engineering products and processes making up a software project are measured and modelled.
Typical software engineering products explored in the module may include: user requirements, design documents, code etc. Typical software engineering processes explored in the module may include: testing, debugging etc. The aim of the module is to use the modelling and measuring of such products and processes to allow quantified decision-making during software development. The module offers students the opportunity to explore both the state-of-the-art and the-state-of-the-practice in software engineering. The module will examine the most up to date research findings about software engineering as well as investigate the current practices of many software engineering companies.
A more detailed description of the module content is provided in the module delivery information for students. |
|
Penetration Testing (COM)
|
15 Credits |
Optional |
| In today's knowledge-centred virtual-computing era, the Cyber Domain is the Information Environment of choice for committing and facilitating crime. Criminals exploit system vulnerabilities in order to manifest threats for furthering their objectives. To secure a system, it is essential for computer security professionals to understand the structure, configuration, tools and techniques computer criminals rely upon, in order to successfully commit to their act. It is equally important to perform regular penetration tests for discovering system and network vulnerabilities. The module will discuss the mind-set, culture and ethics of the professional Pen-Tester, the current practice for conducting a 'PenTest' and of course, research the technologies and tools involved. The module will train scholars to hack infrastructures, mine search-engine results, extract and analyse document metadata, identify and exploit vulnerabilities, enumerate users and services. |
|
Software Engineering Practice (COM)
|
30 Credits |
Optional |
| This module is very practically orientated, and enables students to extend their knowledge of industrially relevant software engineering development processes and practices.
In addition, this module provides students with a chance to apply and practice that knowledge and to develop expertise in software engineering that is applicable to industrial practice. The module allows students the opportunity to appreciate the implications of technical context (e.g. application area) on the selection and effectiveness of software engineering processes and practices.
The topics covered by the module will be practiced within the context of one or two large scale pieces of software. Students will explore and apply various practices to the development and evolution of those pieces of software. Java or another modern and industrially relevant 3GL will be used during this module. |
|
Theory and Practice of Artificial Intelligence (COM)
|
30 Credits |
Optional |
| The overall aim of this module is to explore a range of ideas, theories and techniques used in the construction of Artificial Intelligence (AI) systems. The module will be oriented towards the creation of AI systems for tasks in the areas of intelligent modelling, problem-solving, learning, decision-making, reasoning and others.
A more detailed description of the module content is provided in the module delivery information for students. |
