This module focuses on modern quantitative techniques for text analysis and explores important models for representing and acquiring information from texts. It 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.

This module will give students a grounding in state of the art algorithms that allow computer systems to learn from data. The module will introduce statistical machine learning, probabilistic modelling and their application to describing real world phenomena.

This module introduces a range of statistical and programming techniques and gives practice in their implementation and interpretation using the R programming language. It aims to help students develop the knowledge and experience to select and use appropriate techniques for a variety of problems. The emphasis will be on practical application of techniques and knowledge of their scope rather than development of theoretical underpinnings. Areas to be covered include: exploratory data analysis, simple checks on data, density estimation, simulation, programming and optimization.

This industrially led project aims to provide insights and wider context for the more practical aspects of the taught modules, and to provide students with experience of working in teams to develop a substantial piece of software. Industrial involvement will help ensure the projects remain current and relevant.

This module provides an introduction to the field of computer processing of written natural language, known as Natural Language Processing. It will cover standard theories, models and algorithms, discussing competing solutions to problems, describing example systems and applications, and highlighting areas of open research.

This module will focus on technologies and algorithms that can be applied to data at a very large scale (e.g. population level). From a theoretical perspective it will focus on parallelization of algorithms and algorithmic approaches such as stochastic gradient descent. There will also be a significant practical element to the module that will focus on approaches to deploying scalable ML in practice (e.g. Apache Spark).

This module aims to promote an awareness of the wider social, legal and ethical issues of computing. It describes the relationship between technological change, society and the law, emphasising the powerful role that computers and computer professionals play in a technological society. It also introduces the legal areas which are specific and relevant to the discipline of computing (eg intellectual property, liability for defective software, computer misuse, etc) and aims to provide an understanding of ethical concepts that are important to computer professionals, and experience of considering ethical dilemmas.

This module addresses computer security and forensics issues central to the probity and smooth running of modern industry. The aim of the module is to provide a broad introduction to the topic, which covers the main areas.

This module looks at accelerated computing from multi-core CPUs to GPU accelerators with many TFlops of theoretical performance. It will demonstrate how to write high performance code using hardware such as 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.

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

This is a research led project based on a topic chosen by the student. The project is completed during the summer, and each student will have a personal academic supervisor to guide them during this period, as well as an external supervisor in the case of industrially led projects. The individual project is examined by a dissertation based on the project work, together with a poster presentation, and there is scope for students to demonstrate their critical skills and topic-related knowledge to a high level.