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Core modules:

Introduction to Software Engineering

This module will introduce students to the principles of software engineering, discussing the software lifecycle and approaches to effective teamwork. Students will be introduced to a development framework, which they will work on individually,  and then will apply the principles of software engineering, working in a team to develop a software project.

20 credits

Foundations of Computer Science

In the first semester of this module, we will cover the topics in discrete mathematics that provide an essential foundation for later studies in the department. This includes topics such as sets, functions, sequences and relations, propositional and predicate logic, boolean algebra, combinatorics, induction, recursion, proof strategies, and graph theory.

In the second semester of this module, we will cover the topics in continuous mathematics that provide an essential foundation for later studies in the department. This includes topics such as trigonometric,  exponential and logarithmic functions, polynomials, limits and continuity, differential calculus of one and two variables, integration, series summation and power series, matrices, vector calculus and linear algebra.

20 credits

Java Programming

This module is about programming in the Java language.

There is no requirement that students arrive with any knowledge of programming although many do and some are already very experienced programmers.  This module is intended to ensure that both absolute beginners and strong programmers are capable of writing clear, well structured, readable programs in Java by the end of the module.  

The module is largely taught through practical classes but students will have the opportunity to pace their own learning based on their prior experience.  It does mean that beginners will have to work harder than students who arrive as experts, though some students who consider themselves to be experts may have some unlearning to do

20 credits

Practical Algorithms and Data Structures

This module will introduce students to algorithms and data structures. This module will reinforce the programming concepts that were taught in the autumn semester's programming module while exploring essential data structures and algorithms. This includes a particular focus on algorithms used in traditional AI. Students will also learn to analyse the efficiency of algorithms and data structures, and make informed choices about these for practical problems.

20 credits

Systems and Networks

In this module we investigate topics surrounding the function and operation of modern devices, from the foundations of digital logic and number systems, through to an overview of operating systems and their function and the different types of computer networks and associated protocols (including  IP addressing, ethernet fundamentals, switching technologies, router operations supporting small-to-medium business networks,  wireless local area networks (WLAN), and key security concepts).

20 credits

Introduction to Artificial Intelligence and First Year Reflection

This module will explore intelligence, what it is, how we might measure it, and how we can learn from natural intelligence, in humans and animals, to create new forms of artificial intelligence (AI) for machines. 

A key theme will be to examine similarities and differences between brains and computers, and particularly, the idea that both are able to act intelligently by performing computation.  Through lectures, seminars and computer-based lab classes, the module will investigate some of the key computational building blocks of intelligent systems, including perception and reasoning,  as found in nature and explored through AI. The module will also explore some of the real-world, societal and ethical implications of recent developments in AI and robotics.

Alongside this introduction to artificial intelligence, a parallel thread will support the development of academic and professional skills including the appropriate and ethical use of AI in scholarship and in the workplace. This stream will include reflection upon the content of first year, the skills that have been developed, and their relevance to future study and careers. This includes consideration of why the School believes every one of our undergraduate students should have solid foundations in artificial intelligence, software engineering and the theoretical underpinnings of Computer Science.

20 credits

Core modules:

Programming Language Principles

On entering this module you will already have a good understanding of object oriented programming from your first year studies. In this module, you will be introduced to further paradigms and programming languages. You will explore the choices that are taken in language design and the relationship between high level language and machine-level code that can be directly executed. In this module you will also look at the particular issues associated with concurrent or parallel programming, and the techniques used to combat them.

20 credits

Cybersecurity in Action and Professional Issues

This module has two components. In the first, it aims to teach students about the key security features needed in today's computer systems, how these features can be attacked, and the ways to prevent such attacks. It covers security in a variety of settings, focusing on important topics that are usually taught in the first two years of Computer Science courses, but also looks at some unique aspects of security.

We use a practical, hands-on approach to learning about security, similar to what's known as 'Capture the Flag' (CTF) exercises.

The goal is to develop not only technically proficient but also ethically responsible professionals, who are aware of the consequences of their actions on individuals, organisations, and society at large, thereby preparing them to be conscientious and ethical leaders in the cybersecurity field.

The second component looks more widely at the professional issues in the computing discipline, particularly the commercial context and relevant legal matters.

20 credits

Databases and Logic in Computer Science

This module introduces the foundations of databases and logic in computer science. You will cover theoretical underpinnings including  the syntax and semantics of propositional and predicate logics, natural deduction, notions such as soundness, completeness and (un)decidability, normalisation theory of databases, relational algebra and relational calculus. You will also study practical applications of both databases and logic. This will include the use of SQL, including from within other programming languages, and practical applications of formal logic, such as automated reasoning and procedures, and the use of logic for formal verification of computer systems).

20 credits

Software Hut

The Software Hut (a microcosm of a real Software House) gives students an opportunity to experience the processes of engineering a real software system for a real client in a competitive environment. The taught element covers the tools and technologies needed to manage software development projects successfully and to deliver software products that meet both client expectations and quality standards. Topics that are put into practice include: the requirements engineering process; software modelling and testing; using specific software development framework(s); group project management; quality assurance; testing. Tutorials take the form of project meetings, and so are concerned with team management, conduct of meetings and action minutes.

20 credits

Automata, Computation and Complexity

This module introduces the theoretical foundations for computing systems: finite state machines, pushdown automata, and Turing machines, along with the formal languages that can be recognised by these machine models.

It also deals with the question 'What is computable?' and 'What is efficiently computable?' by showing when problems are computationally hard, and how to find algorithmic solutions to computationally hard problems.

20 credits

Foundations and Applications of Artificial Intelligence

This module will provide the mathematical and statistical underpinnings for artificial intelligence, some of which are used more widely in computer science, and look at some practical applications of AI, with a focus on data science.

Semester 1 will focus on the mathematical and statistical underpinnings, including probability, random variables and distributions (both discrete and continuous), finite sample spaces, Bayes rule, sampling, hypothesis testing, the law of large numbers, the central limit theorem and linear regression.

Semester 2 will provide an introduction to practical data science. Topics include: data preprocessing, feature extraction, feature selection, and supervised/unsupervised learning. The module will employ a practical Python-based approach to help students develop an intuitive grasp of the sophisticated mathematical ideas that underpin this challenging but fascinating subject.

20 credits

Core modules:

You'll complete a Dissertation Project. You'll also undertake a 20-credit Advanced Software Engineering module.

Dissertation Project

In the individual research project, you will complete a major original piece of software design, or an experimental investigation. This work will be reported formally in a research dissertation and also presented at a project presentation session, to which industrial representatives, students and academics are invited. The work will include an Interim report that consists of an initial survey and literature review. You will be engaged in a major piece of software development, or the design and execution of an empirical experiment. You will have regular meetings with your supervisor, who will advise on any problems you encounter. You will prepare an 7,000-14,000 word dissertation, which includes the material from the interim report, but also contains a complete design, implementation and evaluation of the results of your project. This may be assessed by oral examination. 

40 credits

Optional modules:

You'll be able to further tailor your degree through a range of relevant optional modules in areas aligned with the School’s research interests, including speech and language technologies, healthcare technologies, bioinspired machine intelligence, dependable and secure systems, and mathematical foundations.

Core modules:

Genesys

This module involves students working with real customers and solving genuine problems, using agile software engineering and lean startup practices. Students work in teams to develop web applications as part of Genesys, supported by staff from epiGenesys.

45 credits

Parallel Computing with Graphical Processing Units (GPUs)

Accelerator architectures are discrete processing units which supplement a base processor with the objective of providing advanced performance at lower energy cost. Performance is gained by a design which favours a high number of parallel compute cores at the expense of imposing significant software challenges. This module looks at accelerated computing from multi-core central processing units (CPUs) to graphics processing unit (GPU) accelerators with many TFlops of theoretical performance. The module will give insight into how to write high performance code with specific emphasis on GPU programming with NVIDIA CUDA GPUs. A key aspect of the module will be understanding what the implications of program code are on the underlying hardware so that it can be optimised.

15 credits

Optional modules:

You'll be able to further tailor your degree through a range of relevant optional modules in areas aligned with the School’s research interests, including speech and language technologies, healthcare technologies, bioinspired machine intelligence, dependable and secure systems, and mathematical foundations.