Computer Science with an Industrial Placement - MSc

The project consists primarily of an extended period during which students undertake a substantial piece of work and a report on this in the form of a dissertation. It is usually preceded by an exploratory stage in which students review and summarise relevant literature or other technical background, and gain specific skills relevant to their project via a series of taught workshops. It may be permitted to undertake the work in groups, particularly for projects with a development focus. However, the dissertations are produced individually. The project examines the student's ability to research technical background, to understand and expand on a specific problem commensurate with their programme of study and relate it to other work, to carry out investigations and development (as appropriate), to describe results and draw conclusions from them, and to write a coherent and well organised dissertation demonstrating the student's individual reflection and achieved learning.

This module covers the fundamental components (hardware and software) of a typical computer system, and how they collaborate to execute programs. The module provides a comprehensive overview, from the lowest level of abstractions in hardware to the highest level of abstractions of modern programming languages. Examples of topics that may be covered include logic circuits, machine language, processor organization, memory management, processes and concurrency, file systems. Throughout, special attention is paid to abstraction, performance, and other quality requirements.

This module aims to strengthen the foundational programming-in-the-small abilities of students via a strong, practical problem-solving focus. Specific topics will include introductory algorithms, algorithm correctness, and algorithm runtime. Essential data structures and algorithmic programming skills will be covered, for example, arrays, lists and trees, searching and sorting, recursion, and divide and conquer. One part of the module will also introduce students to logical formalisms such as propositional and predicate logic and some of their applications in program development.

Software processes.

Modelling techniques, and the use of these techniques throughout the project lifecycle.

Introduction to modelling principles (decomposition, abstraction, generalization, projection/views) and types of models (information, behavioural, structural, domain and functional).

Risk and risk management in software.

Approaches to software testing and inspection.

Approaches to software configuration management.

Software engineering tools: configuration control, project management, integrated development environments and modelling tools.

Web-based information systems form the heart of e-commerce. They are also increasingly the way businesses handle all their information needs. Building such systems requires an understanding of up-to-date tools and technologies such as web page creation, client side programming, server side programming and databases; it also calls for an understanding of how to design systems that genuinely meet user and business needs.

Cyber security has always been an important aspect of computing systems but its importance has increased greatly in recent years. The curriculum covers areas where cyber security is of major importance, but have different security requirements and may be exposed to different threats and attacks. It also covers techniques and mechanisms used to secure computer systems and data to meet those requirements and protect them. The areas looked at include computer operating systems (and increasingly, distributed operating systems), distributed applications (such as electronic commerce over the Internet), embedded systems (ranging from smart cards to large industrial plant and telecommunications systems), and users. The techniques and mechanisms looked at include cryptography, authentication & authorisation, and access control. Furthermore, the curriculum integrates the legal, ethical, and professional perspectives, for instance, to address concerns about data security, privacy, and societal impact of computing systems.

This module provides an introduction to human-computer interaction, user experience and a range of UX practices (UX - user experience - the study and practice of how people, individually and in groups, experience technologies and other artefacts, and interact with and through them.)

Fundamental aspects of human physiology and psychology are introduced and key features of interaction and common interaction styles delineated. A variety of design methods and UX practices are introduced (e.g. task-based usability testing, think-aloud protocols, first-use experiences, eye-tracking and post-session questionnaires). Throughout the course, the need for a professional, integrated and user-centred approach to interface development and evaluation is emphasised: rapid and low-fidelity prototyping feature as one aspect of this.

The module aim is to give students an overview and understanding of key theoretical, practical and philosophical research and issues around computational creativity, and to give them practical experience in writing and evaluating creative software.

The following is an indicative list of topics that may be covered:

Introduction to computational creativity

Examples of computational creativity software e.g. musical systems, artistic systems, linguistic systems, proof generator systems, systems for 2D and 3D design.

Evaluation of computational creativity systems (both of the quality and the creativity of systems)

Philosophical issues concerning creativity in computers

Comparison of computer creativity to human creativity

Collaborative creativity between humans and computers

Overview of recent research directions/results in computational creativity

Practical experience in writing creative software.

An overview of basic concepts related to eHealth and a perspective on current HIT (Health Information Technology) and innovation. Review of current healthcare related IT systems. The use of information technology for handling clinical data, health systems. Data representation and knowledge management. Security and privacy. Ethics and legal requirements of eHealth systems. Clinical decision support systems. TeleHealth tools for remote diagnosis, monitoring, and disease management. Delivery and monitoring platforms for both hospitals and home environment. Innovation in eHealth systems leading to start-up companies.

This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. Students will learn about the philosophy of AI, how knowledge is represented and algorithms to search state spaces. The module also provides an introduction to both machine learning and biologically inspired computation.

This module explores a range of different data mining and knowledge discovery techniques and algorithms. You learn about the strengths and weaknesses of different techniques and how to choose the most appropriate for any particular task. You use a data mining tool, and learn to evaluate the quality of discovered knowledge.

This module investigates the whole process of information security management and associated activities including the concepts used and practices prescribed by relevant standards, such as those defined by ISO/IEC. A holistic view of information security management is taken, including risk management, the formulation of security policies, business continuity and resilience. Selected socio-technical topics that are important for information security management will also be covered. These shall include AAA (authentication, authorisation and accountability), important legal aspects especially data protection and privacy laws, data protection impact assessment, usability analysis and management, wider human factors in cyber security such as social engineering attacks and the importance of a positive cyber security culture for encouraging secure behaviours of employees and users.

In this module you learn what is meant by neural networks and how to explain the mathematical equations that underlie them. You also familiarise yourself with cognitive neural networks using state of the art simulation technology and apply these networks to the solution of problems. In addition, the module discusses examples of computation applied to neurobiology and cognitive psychology. The module also introduces artificial neural networks from the machine learning perspective. You will study the existing machine learning implementations of neural networks, and you will also engage in implementation of algorithms and procedures relevant to neural networks.

There is an increasing use of nature-inspired computational techniques in computer science. These include the use of biology as a source of inspiration for solving computational problems, such as developments in evolutionary algorithms and swarm intelligence. Similarly, there is now also an increasing interest in understanding how biological, chemical and other natural systems compute, and how this could be exploited for practical applications. It is therefore proposed to allow students the opportunity to become exposed to these types of methods for use in their later careers.

The module will explore existing and emerging legal issues in cyber security, cybercrime, privacy and data protection, including the domestic and cross-boundary legal regulatory frames and their associated ethical dimensions. Topics covered include cybercrime, privacy and data protection, Internet and cyber surveillance, cross-border information flows, and legal structures. Students will be challenged to critically examine the ethics and management of cyber data. It will require students to assess emerging legal, regulatory, privacy and data protection issues raised by access to personal information.

Data types: nominal, numerical, ordinal, text, audio, visual, temporal and non-temporal. Basic descriptive statistics: measures of average and spread, different ways of graphing data. Choosing appropriate and valid methods for the analysis and presentation of data, and understanding the limitations of methods. Data at different scales, including big data, and the computational challenges of processing data at scale. The process of discovering useful knowledge from data: including understanding the need for preprocessing and cleaning data, the challenges of gathering relevant data, and the need to present results in a comprehensible and actionable way. Data mining: classification/regression and clustering, and the idea of predictive analytics. Elements of information retrieval from text. Vector representations of text documents. Fairness and ethical issues concerning data.

COMP8710"

Introduction, including a review of network techniques, switching and multiple access. High speed local area networks. Network protocols, including data link, network, transport and application layers and their security issues. Problems of network security and mechanisms used to provide security such as firewalls and network security protocols. Real time data transmission and quality of service. Naming and addressing and related security concerns. Security of IEEE 802.11 networks.

This module provides an introduction to object-oriented programming using the popular Java language. It is designed for beginners who have not studied computer programming before. By the end students will be able to develop simple programmes using Java.

Building upon Introduction to Object-Oriented Programming, this module covers the design and implementation of high-quality software using OO techniques. Systems are modelled as configurations of objects communicating with one another. Techniques (e.g. inheritance) are introduced which allow objects to play different roles within a system. These two concepts are key to the support for adaptation and reuse that OOP provides. Much emphasis will be placed on gaining a deep understanding of these concepts and applying them in practice by developing programs in Java. The remainder of the module will explore software component frameworks, specifically those that come packaged with Java, placing most emphasis on the frameworks to support the structuring and manipulation of data (data structures and algorithms).

The focus of the module is on the development of the advanced English language competence necessary for post graduate study in scientific disciplines. This includes the ability to interpret and evaluate authentic scientific texts; analyse, discuss and summarise written and visual information both in writing and orally; organise written texts effectively and submit them in grammatically accurate English, and present the results of research orally in a coherent and stimulating way.