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 course focusses on modern quantitative techniques for text analysis and explores important models for representing and acquiring information from texts.

The module is about core technologies underpinning modern artificial intelligence. The module will introduce statistical machine learning and probabilistic modelling and their application to describing real world phenomena. The module will give students a grounding in modern state of the art algorithms that allow modern computer systems to learn from data.

This module starts in Intro Week with a speedy review of basic background mathematics and statistics, and an introduction to R and LaTeX. The module will then introduce students to a range of statistical and programming techniques and give practice in their implementation and interpretation using the software R. 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.

The Industrial Team project provides the opportunity for students to engage in industry inspired research work. It is undertaken in groups. Projects are suggested by industrial problems and supervised by Department of Computer Science staff or industry experts. Students form groups and choose a project which interests them, then refine the scope of the research by conducting a thorough analysis of the topic area and formulating a solution also with the help of their supervisor. The project is developed under strong supervision. The students present their project overview within the first few weeks of the semester and submit a report on the work at the end of semester.

This module provides an introduction to the field of computer processing of written natural language, known as Natural Language Processing (NLP). We will cover standard theories, models and algorithms, discuss competing solutions to problems, describe example systems and applications, and highlight 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 such as SPARK, FLINK, programming languages such as Scala and deployment on elastic computing structures, cloud computing and/or GPUs.

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 provides, in general, an introduction into computer security and forensics. In particular, this module focuses on approaches and techniques for building secure systems and for the secure operation of 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). Moreover, the module requires 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. The lab sessions require a basic command of Linux in general and the command line (shell) in particular.

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

Accelerator architectures are discrete processing units which supplement a base processor with the objective of providing advanced performance at lower energy cost. Performance is gained by a design which favours a high number of parallel compute cores at the expense of imposing significant software challenges. This module looks at accelerated computing from multi-core CPUs to GPU accelerators with many TFlops of theoretical performance. The module will give insight into how to write high performance code with specific emphasis on GPU programming with 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 course.

For the individual project, students can choose from a wide range of possibilities in many different environments both within and outside the University. The project is completed during the summer, and each student will have a personal academic supervisor to guide them during this period. The individual project is examined by a dissertation based on the project work and an oral examination.