Undergraduate Computing Science 2020-2021

This course will be delivered in two halves. The first half will provide a self-contained introduction to computer programming. It will be accessible to all undergraduates. Students will be exposed to the basic principles of computer programming, e.g. fundamental programming techniques, concepts, algorithms and data structures. The course contains lectures where the principles are systematically developed. As the course does not presuppose knowledge of these principles, we start from basic intuitions. The second half will be particularly of use to those studying Science and Engineering subjects, broadly interpreted, as well as Computing and IT specialists. It will include a gentle introduction to professional issues and security concepts.

This course will introduce students to techniques that support problem solving and modelling with computers, and concepts and methods that are fundamental to computing science. The techniques and concepts will be illustrated with numerous computing examples.

Beginning with digital logic gates and progressing to the design of combinational and sequential circuits, this course use these fundamental building blocks as the basis for what follows: the design of an ARM microprocessor. In addition, students will get hands on experience with programming using ARM assembly language which is the inner language spoken by the processor. By the end of the course, students will have a top-to-down understanding of how a microprocessor works. The course is taught without prerequisites; students are taught with plenty of exercises from lectures, tutorials, practical and tests every week. 

This course will build on the basic programming skills acquired in the first half-session and equip the students with advanced object oriented programming knowledge, implementation of data structure and algorithms, and basic software engineering techniques. The students will be challenged with more complicated programming problems through a series of continuous assessments.

This course builds on the previous course so that you can build more complex database driven web applications using a suitable framework to guide you. This also continues to round out your computing science craftsmanship skills with more emphasis on learning appropriate practices such as version source control, testing and group collaboration, so that you can build good habits, which will help your further during your degree.

This course provides a basic-level introduction to some areas of Discrete Mathematics that are of particular relevance to Computing. The course starts with a simple introduction to formal languages (starting from Regular Expressions and Finite-State Automata); it continues with an introduction to Predicate Logic (assuming basic familiarity with Propositional Logic); it concludes with an introduction to probability, focussing on Bayesian reasoning.

Databases are an important part of traditional information systems (offline /online) as well as modern data science pipelines. This course will be of interest to anyone who wishes to learn to design and query databases using major database technologies. The course aims to teach the material using case studies from real-world applications, both in lectures and lab classes.

In addition, the course covers topics including management of different kinds of data such as spatial data and data warehousing. The course provides more hands-on training that develops skills useful in practice.

This course looks at why a computer system that interacts with human beings needs to be usable. It covers a set of techniques that allow usability to be taken into account when a system is designed and implemented, and also a set of techniques to assess whether usability has been achieved. Weekly practical sessions allow students to practice these techniques. The assessed coursework (which is normally carried out by groups of students) gives an opportunity to go through the design process for a concrete computer system, with a particular focus on ensuring usability.

The course will touch on a wide variety of languages, both past and present, with an emphasis on imperative, functional, object oriented and logical paradigms.  Rather than dwell on the features of any particular language, we will focus instead on fundamental concepts, the key characteristics of successful language, the main features in programming languages, the different types of programming execution and patterns of problem-solving.

You will gain practical experience with each programming paradigm to choose between alternative ways and figure out how to do things in languages that don't support them explicitly.

This course builds on the previous course so that you can build more complex database driven web applications using a suitable framework to guide you. This also continues to round out your computing science craftsmanship skills with more emphasis on learning appropriate practices such as version source control, testing and group collaboration, so that you can build good habits, which will help your further during your degree.

This course provides the knowledge needed to understand, design and compare algorithms.  By the end of the course, a student should be able to create or adapt algorithms to solve problems, determine an algorithm's efficiency, and be able to implement it. The course also introduces the student to a variety of widely used algorithms and algorithm creation techniques, applicable to a range of domains. The course will introduce students to concepts such as pseudo-code and computational complexity, and make use of proof techniques as well as the student’s programming skills.

Students registered for Honours and Joint Honours degree programmes offered by Computing Science can take up short-term placements / internships with companies (1 month). Students are required to successfully complete an internship, submit a final report and give an oral presentation.

Knowledge Representation (KR) is concerned with how knowledge can be represented symbolically and manipulated in an automated way by reasoning programs. In fact, KR has long been considered central to AI because it is a significant factor in determining the success of knowledge-based systems.

This course describes the formalisation of knowledge and its use in knowledge-based systems. It follows the whole "life-cycle" of knowledge, from the initial identification of relevant expertise, through its capture, representation (in ontology and /or rule languages), use (based on reasoning), evaluation, and reuse.

This course discusses core concepts and architectures of operating systems, in particular the management of processes, memory and storage structures. Students will learn about the scheduling and operation of processes and threads, problems of concurrency and means to avoid race conditions and deadlock situations. The course will discuss virtual memory management, file systems and issues of security and recovery. In weekly practical session, students will gain a deeper understanding of operating system concepts with various programming exercises.

Students will develop large commercial and industrial software systems as a team-based effort that puts technical quality at centre stage. The module will focus on the early stage of software development, encompassing team building, requirements specification, architectural and detailed design, and software construction. Group work (where each team of students will develop a system selected using a business planning exercise) will guide the software engineering learning process. Teams will be encouraged to have an active, agile approach to problem solving through the guided study, evaluation and integration of practically relevant software engineering concepts, methods, and tools.

This course provides a basic-level introduction to computability via the notion of a Turing Machine. Some familiarity with imperative programming (e.g., in JAVA) and with the basics of set theory (e.g., the notion of a bijection) is assumed. The Functional language Haskell (familiar from earlier courses, including CS2013) is used to explore the concepts of infinity, recognisability and decidability, which are crucial to computability.

Students registered for Honours and Joint Honours degree programmes offered by Computing Science can take up short-term placements / internships with companies (1 month). Students are required to successfully complete an internship, submit a final report and give an oral presentation.

This course discusses core concepts of distributed systems, such as programming with distributed objects, multiple threads of control, multi-tire client-server systems, transactions and concurrency control, distributed transactions and commit protocols, and fault-tolerant systems. The course also discusses aspects of security, such as cryptography, authentication, digital signatures and certificates, SSL etc. Weekly practical sessions cover a set of techniques for the implementation of distributed system concepts such as programming with remote object invocation, thread management and socket communication.

This course provides insight into the business reasons for large software systems such as loyalty card systems, backend systems integrating firms and their suppliers and larges systems that integrate payroll, finance and operational parts of a business. You also learn the entrepreneurial aspects of business during the practical sessions where you explore and develop your own business application idea using service design and lean startup approaches centred around customer development, which you will find useful in any future work. This course is open to anyone across the university and requires no programming experience.

In this module, which is the follow-up of CS3028, students will focus on the team-based development of a previously specified, designed, and concept-proofed software system. Each team will build their product to industrial-strength quality standards following an agile process and applying the software engineering concepts, methods, and tools introduced in CS3028. The course includes a series of mandatory participatory seminars on professional and management issues in IT and IT projects. Students will be expected to relate their engineering work to these issues.

Students registered for Honours and Joint Honours degree programmes offered by Computing Science can take up short-term summer placements / internships with companies (3 months). Students are required to successfully complete an internship, submit a final report and give an oral presentation.

Students registered for Honours and Joint Honours degree programmes offered by Computing Science can take up short-term placements / internships with companies (1 month). Students are required to successfully complete an internship, submit a final report and give an oral presentation.

The course provides a solid foundation in computer and information security. It will cover topics of Information and Risk, Threats and Attacks, Cybersecurity Architecture and Operations, Secure Systems and Products, Cybersecurity Management and Trustworthy Software. 

In this course, you will conduct an individual research project into the behaviour of a computing system. You will develop knowledge and understanding of rigorous methods to: explore computing system behaviour; identify questions about behaviour; design experiments to answer those questions; analyse experimental results; and report on the outcomes of your research. You will develop your understanding of research ethics and how this relates to professional behaviour.

Computational Intelligence covers a wide range of issues that developed in parallel with, or in competition to, symbolic AI. The major constituents of the field are bio-inspired computing – which deals with an ever expanding number of biologically related techniques – and fuzzy logic – which deals with reasoning under conditions of vagueness. In this course we will explore a number of topics that are core to Computational Intelligence (e.g. neural nets and evolutionary computing) and these will lead into some state-of-the-art approaches (such as fuzzy model-based reasoning and learning).

This course surveys many of the core problems of robotics, and their solutions. By the end of the course, a student should be able to program robots that move in predictable ways, overcoming environmental uncertainties; that can interpret their surroundings; and that can plan their motion in order to achieve goals. Topics covered include robot motion; image processing and computer vision; localisation methods and computer based search and planning. Apart from using programming skills to implement robot algorithms, the students will learn how to mathematically model robots in order to understand why robot algorithms are designed as they are.

Consists of a supervised project which provides experience of investigating a real problem in computing science, or a computing application/technology. Learner will apply knowledge and skills gained earlier in their degree programme, and seek to go even further. Managing the project and presenting the results obtained are an integral part of the investigation.

Consists of a supervised project which provides experience of investigating a real problem in computing science, or a computing application/technology. Learners will apply knowledge and skills gained earlier in their degree programme, and seek to go even further. Managing the project and presenting the results obtained are an integral part of the investigation.

This course consists of a supervised individual project; it provides students the experience of investigating a real problem in the intersection of computing science and physics, exploring solutions and technologies.

Students can gain work experience in industrial, business or public sector organisations by taking up a 1-year placement / internship. Students are required to submit monthly reports as well as a final thesis summarising their work experience. Students who successfully complete such a placement will earn an advanced undergraduate degree (MSci in Computing Science with Industrial Placement).

Consists of a supervised project that provides experience of investigating a real problem in computing science, or a computing application/technology. Presenting the results obtained is an integral part of the investigation.