Computer Science including International Year MSci

The module introduces basic principles of programming and algorithms. It covers fundamental programming constructs, such as types and variables, expressions, control structures, and functions.

You'll learn how to design and analyse simple algorithms and data structures that allow efficient storage and manipulation of data. You'll also become familiar with basic software development methodology.

You will spend around six hours per week in lectures, computer classes and tutorials.

You will gain a basic understanding of the fundamental architecture of computers and computer networks.

You’ll learn how the simple building blocks of digital logic can be put together in different ways to build an entire computer.

You’ll also learn how modern computer systems and networks are constructed of hierarchical layers of functionality which build on and abstract the layers below.

You will spend five hours per week in tutorials, lectures and computer classes.

This module runs alongside 'Computer Fundamentals' and provides an expanded view by considering how real computer systems (such as ARM, x86, Linux and *BSD) and networks work.

You’ll also cover the principles of the lower level implementation of I/O using polling and interrupts, and the use of exceptions; how memory and storage are organized as well addressing the issues arising from multicore systems. 

You’ll spend around five hours per week in tutorials, lectures and computer classes.

You’ll cover the basic concepts in mathematics which are of relevance to the computer scientists.

These include:

• logic
• sets
• functions and relations
• graphs
• induction
• basic probability
• statistics and matrices

This module considers both the structure of databases, including how to make them fast, efficient and reliable, and the appropriate user interfaces which will make them easy to interact with for users. You will start by looking at how to design a database, gaining an understanding of the standard features that management systems provide and how you can best utilise them, then develop an interactive application to access your database.

Through the lectures and computing sessions you will learn how to design and implement systems using a standard database management system, web technologies and GUI interfaces through practical programming/system examples.

This module focuses on the fact that programming is only one step of the larger Software Engineering Process. To develop good software, you must gather requirements, design it well, plan the development, do the programming, have a testing strategy, test the parts and the product as a whole, and have a maintenance strategy for fixing the things that no one (even the client) imagined were important until it after it was delivered.

Software Engineering is a process that is much more than just programming. You'll spend two-three hours per week discussing the stages of the Software Engineering process in lectures, whilst carrying out activities in labs that help you understand the underlying issues.

In this module you will learn the basic principles of the object-oriented and functional approaches to programming, using the languages Java and Haskell. You will also see how they can be used in practice to write a range of different kinds of programs.

You will gain a broad overview of the fundamental theories and techniques of artificial intelligence (AI).

You’ll explore how computers can produce intelligent behaviour, and will consider topics such as the history of AI, AI search techniques, neural networks, data mining, philosophical and ethical issues, and knowledge representation and reasoning.

You will spend two hours per week in lectures for this module. 

Working in groups of around five to six people, you’ll be assigned a supervisor who will provide you with a short written description of a computer application to be designed, programmed, and documented during the course of the module.

Each group will meet twice a week, once with your supervisor and once without; you’ll also have four introductory one hour lectures.

This module covers important aspects of algorithms, namely their correctness and efficiency.

You’ll study topics such as:

• proofs in propositional logic and predicate logic
• classical vs. intuitionistic reasoning
• basic operations on types
• verification of list based programs
• introduction to program specification and program correctness

To address the issue of efficiency we cover the use of mathematical descriptions of the computational resources needed to support algorithm design decisions. The emphasis is upon understanding data structures and algorithms so as to be able to design and select them appropriately for solving a given problem.

This course covers the fundamentals of operating systems and concurrency.

Topics in operating systems include:

• the architecture of operating systems
• process management
• memory management
• file systems
• a brief introduction to virtualisation and cloud computing

The principles of concurrency will be introduced from both the perspective of an operating system. Specific topics on concurrency include:

• hardware support for concurrency
• mutual exclusion
• the use of threads
• synchronisation

You'll investigate classes of formal language and the practical uses of this theory, applying this to a series of abstract machines ultimately leading to a discussion on what computation is and what can and cannot be computed.

You'll focus in particular on language recognition, but will study a range of topics including:

• finite state machines
• regular expressions
• context-free grammars
• Turing machines
• Lambda calculus

This module builds on parts of the ACE module addressing data structures and formal reasoning and introduces concepts which are important to understand the analysis of algorithms in terms of their complexity.

Through two hours of lectures each week, you’ll be given an overview of the field of Human Computer Interaction, which aims to understand people's interaction with technology and to apply this knowledge in the design of usable interactive computer systems. The module will introduce the concept of usability and will examine different design approaches and evaluation methods.

Through a two hour lecture each week, you’ll be introduced to concepts and techniques for software testing and will be given an insight into the use of artificial and computational intelligence for automated software testing. You’ll also review recent industry trends on software quality assurance and testing.

This module builds on the Fundamentals of Artificial Intelligence module. The emphasis is on building on the AI research strengths in the School.

You will be introduced to key topics such as AI techniques, fuzzy logic and planning, and modern search techniques such as Iterated Local Search, Tabu Search, Simulated Annealing, Genetic Algorithms, and Hyper-heuristics, etc.

You will also explore the implementation of some AI techniques.

You'll study the theory used in the design and analysis of advanced algorithms and data structures. Topics covered include string algorithms (such as for string matching, longest common subsequence), graph algorithms (such as for minimum cuts and maximum flows, and Google's pagerank algorithm), advanced data structures (such as Fibonacci heaps and Bloom filters), and randomised search heuristics (evolutionary algorithms). You'll learn all the necessary probability theory will be introduced, including random variables and concentration inequalities.

The theory is practiced in weekly labs where we learn how to implement the algorithms and data structures as functional and imperative programs (using the languages Haskell and C), and apply these to solve large instances of real-world problems. 

This module will provide you with an advanced knowledge of computer communications networks, using examples from all-IP core telecommunications networks to illustrate aspects of transmission coding, error control, media access, internet protocol, routing, presentation coding, services and security. You’ll spend around three hours per week in lectures and computer classes. 

This module introduces you to the computer science of robotics, giving you an understanding of the hardware and software principles appropriate for control and localisation of autonomous mobile robots. A significant part of the module is laboratory-based, utilising physical robotic hardware to reinforce the theoretical principles covered.

Spending around three to four hours each week in lectures and practicals, you’ll cover a range of topics including:

• basic behavioural control architectures
• programming of multiple behaviours
• capabilities and limitations of sensors and actuators
• filtering techniques for robot localisation

Students taking part in activities relating to programming experience such as developing apps in their spare time, contributing to open source projects, or building things in hackathons may receive academic credit for showing they have experience and excellent development skills. The emphasis of this module is that you provide evidence of your significant extra-curricular software development experience. Students will only be able to register for this module with the approval of the convenor/school, once the material for assessment has been checked.

You’ll review classical Boolean logic and set theory, including the common operations of union, intersection and complement.

Fuzzy Logic Systems (FLSs) will be introduced and illustrated in conjunction with examples of real-world applications in industrial control and other areas.

You’ll spend around four hours each week in lectures and workshops, and will be given the opportunity to design, programme and deploy a fuzzy logic system, providing a tangible real-world example of some underlying concepts of FLSs.

Students undertake a programming project for an external client in self-formed groups of two to four students under the supervision of an academic member of staff. The client, which can be a company, charity, research group etc., but not the supervisor, provides a problem that requires a sufficiently challenging piece of software to be developed. The client and project idea could be provided by the students or the supervisor. However, projects must have aspects that are relevant to each student's programme of study; eg, there needs to be an artificial intelligence (AI) aspect if any AI students are involved.

The main assessed outputs are the developed software, including any end-user documentation, along with a 5,000-word document that outlines the development, design and implementation of the software, highlighting the most interesting aspects. The software must be developed in a professional and systematic manner appropriate for the problem domain. The assessment is informed by a statement from the external client on how well the developed software addresses the problem. Additionally, each student submits an individual 5,000-word report explaining his or her own contributions and giving a critical appraisal of how the project went, including group dynamics and the contributions of others.

You will undertake a programming project relevant for AI for an External Client under the supervision of an academic member of staff.

The client, which can be a company, charity, research group etc., provides a problem that requires a sufficiently challenging piece of software to be developed. The client and project idea could be provided by the students or the supervisor. Each project must ultimately be agreed with the concerned Supervisor.

The main assessed outputs are the developed software, including any end-user documentation, along with a 15,000-word document that outlines the development, design and implementation of the software, highlighting the most interesting aspects.

The software must be developed in a professional and systematic manner appropriate for the problem domain.

The assessment is informed by a statement from the External Client on how well the developed software addresses the problem.

Students undertake a research project in computer science supervised by an academic member of staff. The topic should fall within the supervisor's research interests and must further be relevant to the student's programme of study; in particular, projects undertaken by artificial intelligence (AI) students must have a strong AI focus. The project may be proposed by either the supervisor or the student, and may be theoretical, empirical, or even of survey type depending on what is appropriate and feasible for the area and topic. Projects, however, must ultimately be agreed with the supervisor concerned.

The results from the project are to be distilled into a conference-format research paper, authored by the student and constituting the main assessed output. There may, however, be further deliverables as dictated by the nature of the project. Any such deliverables are to be submitted (electronically) as supplementary material. A revised version of the paper, possibly co-authored with the supervisor, may subsequently be submitted for publication to an external venue, such as a conference or journal, if the work is judged to be of sufficiently high standard. 

Students taking part in approved activities, such as running code clubs in schools, organising school computing activity days, or becoming active STEM ambassadors, may receive academic credit for demonstrating they have actively contributed to the development of younger students. Students will have undertaken an agreed number of hours on the activities, identified personal goals and targets in relation to these activities and maintained a reflective portfolio as a record of evidence of their competence and achievements. Students will only be able to register for this module with the approval of the convenor/school, once the material for assessment has been discussed.

This module introduces you to three broad simulation paradigms commonly used in operations research and management science: system dynamics, discrete event, and agent-based. Covering topics such as the introduction to the principles of modelling and simulation, conceptual modelling, model implementation, and output analysis, you will become competent in choosing and implementing the correct method for your particular problem. You will spend around four hours per week in lectures and computer classes.