Computer Science BSc

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

Global Engineering Challenge Week

The Faculty-wide Global Engineering Challenge Week is a compulsory part of the first-year programme. The project has been designed to develop student academic, transferable and employability skills as well as widen their horizons as global citizens. Working in multi-disciplinary groups of 5-6, for a full week, all students in the Faculty choose from a number of projects arranged under a range of themes including Water, Waste Management, Energy and Digital with scenarios set in an overseas location facing economic challenge. Some projects are based on the Engineers Without Borders Engineering for people design challenge*.

*The EWB challenge provides students with the opportunity to learn about design, teamwork and communication through real, inspiring, sustainable and cross-cultural development projects identified by EWB with its community-based partner organisations.

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

Robotics

This module is concerned with the design and implementation of the technology underpinning contemporary robotics. The course has a multidisciplinary content spanning psychology, computer science and robotics. 

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.

This module has no summer resit. Failure in this module will normally require students to repeat it the following year with attendance.

This module has the explicit objective of developing group teamwork skills. Participation in teamwork is mandatory and failure to participate will result in deduction of marks and eventually loss of credits. Passing this module is essential for being awarded a degree accredited by the British Computer Society (BCS).

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

Engineering - You're Hired

The Faculty-wide Engineering - You're Hired Week is a compulsory part of the second year programme, and the week has been designed to develop student academic, transferable and employability skills. Working in multi-disciplinary groups of about six, students will work in interdisciplinary teams on a real world problem over an intensive week-long project. The projects are based on problems provided by industrial partners, and students will come up with ideas to solve them and proposals for a project to develop these ideas further.

Core modules:

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

Accounting and Law for Engineers

The module is designed to introduce engineering students to key areas of accounting and legal risk that engineers should be aware of in their working environment. The module will draw directly on practical issues of budgeting, assessing financial risks and making financial decisions in the context of engineering projects and/or product development. At the same time, the module will develop students' understanding of the legal aspects of entering into contracts for the development and delivery of engineering projects and products, and enhance their awareness of environmental regulation, liability for negligence, intellectual property rights and the importance of data protection. Through a series of parallel running lectures in the two disciplines, the module will provide a working knowledge of the two areas and how they impinge on engineering practice.

10 credits

Optional modules:

Modelling and Simulation of Natural Systems

This module will provide a practical introduction to techniques used for modelling and simulating dynamic natural systems. Many natural systems can be modelled appropriately using differential equations, or individual based methods. In this module, you will explore and understand both modelling approaches. You will gain knowledge of the assumptions underlying these models, their limitations, and how they are derived. You will learn how to simulate and explore the dynamics of computational models, using a variety of examples mostly drawn from natural systems. At the end of the module, we will introduce basic recurrent neural networks as examples of dynamical systems with multiple timescales.

10 credits

Computer Vision

This module provides a comprehensive introduction to computer vision. Major topics include image processing, detection and recognition, semantic understanding, photometric geometry, image registration, place recognition, and vision and video analysis. Students will learn the basic theories and fundamental topics of computer vision, the mainstream methods for core computer vision applications. Importantly, the students will also gain hands-on experience to solve real-life vision problems.

10 credits

Text Processing

This module introduces fundamental concepts and ideas in natural language text processing, covers techniques for handling text corpora, and examines representative systems that require the automated processing of large volumes of text. The module focuses on modern quantitative techniques for text analysis and explores important models for representing and acquiring information from texts.

10 credits

Modelling of Concurrent Systems

The aim of this module is to set out a strong theoretical basis for the analysis and design of concurrent, distributed and mobile systems. We will use the process calculi to model and reason about complex systems, studying both its formal semantics and its many uses, via a number of examples.

10 credits

Reinforcement Learning

This module aims to teach students the theory and implementation of reinforcement learning. Topics include: Supervised learning: the backpropagation algorithm (as prerequisite for Deep reinforcement learning). Reinforcement Learning: Temporal Difference Learning (Q learning, SARSA), Deep Reinforcement Learning, Advanced Topics. As well as the material taught in class, students are expected to self-study relevant books and research articles and produce reports in research article styles.

10 credits

Computer Security and Forensics

This module provides an introduction into computer security and forensics focussing on approaches and techniques for building secure systems and for the secure operation of systems. It aims to develop knowledge and understanding of fundamental principles of information security, develop familiarity with compromise of computer systems and what countermeasures can be adopted and provide practical experience of implementing secure systems. The module requires a solid understanding of mathematical concepts (e.g., modulo-arithmetic, complex numbers, group theory) and logic (set theory, predicate logic, natural deduction) a solid understanding of a programming language (e.g., Java, Ruby, or C), basic software engineering knowledge and an understanding of database and Web systems. Students should be aware that there are limited places available on this course.

10 credits

Speech Processing

This module aims to demonstrate why computer speech processing is an important and difficult problem, to investigate the representation of speech in the articulatory, acoustic and auditory domains, and to illustrate computational approaches to speech parameter extraction. It examines both the production and perception of speech, taking a multi-disciplinary approach (drawing on linguistics, phonetics, psychoacoustics, etc.). It introduces sufficient digital signal processing (linear systems theory, Fourier transforms) to motivate speech parameter extraction techniques (e.g. pitch and formant tracking).

10 credits

3D Computer Graphics

This module is an introduction to the techniques used in modern 3D computer graphics. It deals with fundamental techniques that are the basis of work in a range of industries, e.g. entertainment and computer-aided design. Both basic and advanced topics concerned with the production of images of 3D objects are covered, including: 3D representations and manipulations in graphics, light reflection models, realism techniques such as shadows and textures, ray tracing and 3D animation.

10 credits

The Intelligent Web

This module is concerned with gaining knowledge and understanding of the opportunities and challenges of the intelligent Web. We will introduce, study and apply contemporary technologies such as: 1) The basic tools of the advanced Web and their implementation; 2) Offline and multimodal interaction; 3) Client server architectures; 4) Web 3.0 and the Web of Data. Students should be aware that there are limited places available on this course.

10 credits

The Internet of Things

The Internet of Things (IoT) connects various types of sensors, devices and embedded systems with cloud-based analytics, utilising data analytic and machine learning to process real-world information. As IoT continues to expand, engineers with expertise in its technologies and security will be in high demand.

This module explores the fundamentals of IoT, covering key hardware, communication protocols, security, and cloud-based data analytics. Students will gain hands-on experience developing IoT applications using Azure IoT Hub and AWS IoT, integrating sensors with general computing devices.

10 credits

Software Reengineering

Software development often involves the improvement and adaptation of 'legacy systems' - well-established, business-critical software systems that might have become difficult to maintain over time. This module introduces the skill-set that is required to get to grips with such systems. It teaches students how to reverse-engineer and appraise complex, unwieldy systems by implementing source code and execution analysis techniques. It also presents a range of strategies that can be used to adapt and reengineer such systems to improve their quality and viability.

10 credits

Bioinspired Computing

This module focuses on modern artificial intelligence (AI) techniques and their inspiration from biological systems. Examples include evolution, multicellular tissues, neural systems, the immune system and swarms, inspiring abstractions such as evolutionary or swarm-based optimization algorithms, neural computing, as well computational approaches to simulate real world systems, (e.g. cellular automata and agent-based models). Lectures introduce a range of AI and related approaches in the context of their relevant biological inspiration and also their potential application to real word problems. A selection of optimisation and simulation techniques are explored in more depth using Python via active learning in computer laboratories. There is an emphasis on applying the scientific approach to practical work within this module.

10 credits

Cyber Security Team Project

Cyber Security Team Project is a module that equips students with the knowledge needed to keep an organisation secure from today's cyber security threats and presents the necessary steps to take when a breach occurs.

Using a combination of learning methods and teaching techniques such as project based learning, active learning and case studies, this module teaches cyber security management principles that are needed to secure the digital assets of an organisation. A major part of this module involves students working in teams to evaluate and develop secure policies and strategies to solve real-world cyber security issues for organisations.

Students should be aware that there are limited places available on this module.

10 credits

Cognitive and Biomimetic Robotics

Cognitive robotics is the field of creating robots that think, perceive, learn, remember, reason and interact. Biomimetic robotics is the approach of designing robots using principles discovered in nature, including what we can learn from the evolution and development of natural intelligence in animals including humans. This module will explore progress in developing cognitive and biomimetic robots, relating wider progress in artificial intelligence, machine learning, and cognitive science to the development of next generation robotic systems. The practical component of the course will focus on programming biomimetic cognitive architectures for robots.

10 credits

Software Testing and Analysis

This module introduces the problems and techniques of analysing and testing software systems. The module covers how to statically analyse software and how to dynamically test it. The module will teach different techniques and tools to thoroughly test software systems, and will teach how to automate testing tasks, including test generation. Finally, the module will cover techniques to measure and assess aspects of source code and software tests.

10 credits

Managing Engineering Projects and Teams

This module provides you with an understanding of the significance of projects as an instrument of business success in engineering organisations. You will learn a range of project management tools, techniques and methodologies throughout the project life cycle. You will develop skills in defining, planning, delivering, and controlling engineering projects. You will also learn the roles and responsibilities of people within engineering projects and understand how to manage teams in engineering projects.

10 credits

Software Development for Mobile Devices

This module aims to provide a thorough grounding in the principles of software development for mobile devices. The Android platform will be used as an example, although the module emphasises general principles that are common across all mobile platforms. An important aim of the module is to demonstrate the real-world application of object-oriented programming principles and design patterns in software for mobile devices. Students undertake a substantial software implementation project, working in teams.

10 credits

Advanced Algorithms

Algorithms and algorithmic problem solving are at the heart of computer science. This module, Advanced Algorithms, focuses on teaching efficient algorithm design and analysis for solving complex computational problems. It covers optimisation tools, various algorithmic techniques, and a number of modern computational models that deals with massive datasets - these are crucial for students who are aspiring researchers or industry professionals. The module integrates research-led teaching to introduce students to cutting-edge topics in algorithm design and optimisation theory. The students gain theoretical as well as practical skills relevant to research and industry demands, making them well-prepared for any career requiring a deep understanding of algorithms and their efficient implementation.

10 credits

Undergraduate Ambassadors Scheme in Computer Science

This module provides an opportunity for students to gain first-hand experience of computer science education through a mentoring scheme with a teacher in a local school. Typically, each student will work with one group for half a day every week, for 10 weeks. Students will be given a range of responsibilities from teaching assistant to the organisation and teaching of self-originated special projects. Only a limited number of places are available and students will be selected on the basis of their commitment and suitability for working in schools. 

This module has no summer resit. Failure in this module will normally require students to repeat it the following year with attendance.

20 credits