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This module aims to:
- Introduce the logical and mathematical foundations of computer science.
- Illustrate the use of formal languages in computer science, including in algorithms and programming.
- Extend students' mathematical sophistication and skills.
- Present basic concepts and techniques of combinatorics, statistics and probability.
- Give mathematical background necessary for other compulsory modules.
- Develop the study skills necessary for students to learn new concepts of mathematics and programming (including those we do not cover in the degree).
- Instill a range of useful problem solving skills.

This module aims to:
- Introduce the logical and mathematical foundations of computer science.
- Illustrate the use of formal languages in computer science, including in algorithms and programming.
- Extend students' mathematical sophistication and skills.
- Present basic concepts and techniques of combinatorics, statistics and probability.
- Give mathematical background necessary for other compulsory modules.
- Develop the study skills necessary for students to learn new concepts of mathematics and programming (including those we do not cover in the degree).
- Instill a range of useful problem solving skills.

This module aims to cover the continuous mathematics that's required for the computer science and software engineering programmes.

This module aims to cover the continuous mathematics that's required for the computer science and software engineering programmes.

Machine Learning is about extracting useful information from large and complex datasets. Although driven by applications, the techniques used are based on a broad mathematical basis. This course provides the mathematical foundations of the subject from functional analysis through to optimisation, convexity and information theory.

Machine Learning is about extracting useful information from large and complex datasets. Although driven by applications, the techniques used are based on a broad mathematical basis. This course provides the mathematical foundations of the subject from functional analysis through to optimisation, convexity and information theory.

Machine Learning is about extracting useful information from large and complex datasets. Although driven by applications, the techniques used are based on a broad mathematical basis. This course provides the mathematical foundations of the subject from functional analysis through to optimisation, convexity and information theory.

This module is compulsory for every Year 3 student of any Mathematical Sciences degree programme. Its main goal is to provide the student with an opportunity to research an area of mathematics that interests them, while strengthening their transferrable skills and supporting them in growing their CV and achievements that will make them more attractive to employers. As to the latter, there will be specific sessions devoted to various topics related to employability, CV preparation, and other aspects of job hunting. The remainder of this module overview is however about the former, the main part of this module.

This module provides an opportunity to develop skills and knowledge in an area of mathematical science that excites the student and matches their particular strengths. We will provide support to guide the student through their research and report preparation, while giving them the freedom to explore the subject on their own. Support is provided through plenary lectures and through individual (roughly bi-weekly) supervision meetings. The work may involve directed reading of books or original papers in journals and the provision of examples to illustrate particular aspects of a topic. Some topics may also present the opportunity for students to pursue their own investigations, undertake practical work using the computer or working on a project brief from an industry partner.

In summary, the student will learn how to:
(a) Write up a report on their topic: a preliminary report at the end of Semester 1 leading to feedback and advice followed by a completed final report at the end of Semester 2.
(b) Carry out a literature survey appropriate to their topic and how to use a wide variety of sources in an imaginative way, how to give proper credit to the work of others, and in particular what constitutes and how to avoid plagiarism.
(c) Present their work to a small audience. This is great training for communicating a technical subject succinctly, and a skill a student will definitely use after graduating. There is an opportunity to present their work both at the end of Semester 1 and at the end of Semester 2.

This module is compulsory for every Year 3 student of any Mathematical Sciences degree programme. Its main goal is to provide the student with an opportunity to research an area of mathematics that interests them, while strengthening their transferrable skills and supporting them in growing their CV and achievements that will make them more attractive to employers. As to the latter, there will be specific sessions devoted to various topics related to employability, CV preparation, and other aspects of job hunting. The remainder of this module overview is however about the former, the main part of this module.

This module provides an opportunity to develop skills and knowledge in an area of mathematical science that excites the student and matches their particular strengths. We will provide support to guide the student through their research and report preparation, while giving them the freedom to explore the subject on their own. Support is provided through plenary lectures and through individual (roughly bi-weekly) supervision meetings. The work may involve directed reading of books or original papers in journals and the provision of examples to illustrate particular aspects of a topic. Some topics may also present the opportunity for students to pursue their own investigations, undertake practical work using the computer or working on a project brief from an industry partner.

In summary, the student will learn how to:
(a) Write up a report on their topic: a preliminary report at the end of Semester 1 leading to feedback and advice followed by a completed final report at the end of Semester 2.
(b) Carry out a literature survey appropriate to their topic and how to use a wide variety of sources in an imaginative way, how to give proper credit to the work of others, and in particular what constitutes and how to avoid plagiarism.
(c) Present their work to a small audience. This is great training for communicating a technical subject succinctly, and a skill a student will definitely use after graduating. There is an opportunity to present their work both at the end of Semester 1 and at the end of Semester 2.

‘I have here in my hand a list of 205 names that were made known to the Secretary of State as being members of Communist Party and who nevertheless are still working and shaping policy in that State Department.'

With these words, asserting both the existence of an internal communist menace and the government failure to act against it, Senator Joseph McCarthy thrust himself into the centre of US national politics. His inquisition into communist subversives and spies lasted from 1950 to 1954. But ‘McCarthyism' as a phenomenon was more deeply-rooted, more enduring and much broader in scope than the career and campaigns of a single politician. This module explores the causes, course and effects of McCarthyism writ large, from the end of the Second World War through to the late 1950s.