Artificial Intelligence

Core modules:

Scalable Machine Learning

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 parallelisation 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, programming languages such as Python/Scala and deployment on high performance computing platforms/clusters.

15 credits

Text Processing

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 module focuses on modern quantitative techniques for text analysis and explores important models for representing and acquiring information from texts. You should be aware that there are limited places available on this course. 

15 credits

Machine Learning and Adaptive Intelligence

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. It has a considerable focus on the mathematical underpinnings of key ML approaches, requiring some knowledge of linear algebra, differentiation and probability.

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

15 credits

Natural Language Processing

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

15 credits

Professional Issues

This module aims to enable students to recognise the legal, social, ethical and professional issues involved in the exploitation of computer technology and be guided by the adoption of appropriate professional, ethical and legal practices. It describes the relationship between technological change, society and the law, including the powerful role that computers and computer professionals play in a technological society. It introduces key legal areas which are specific and relevant to the discipline of computing (e.g., intellectual property, liability for defective software, computer misuse, etc) and aims to provide an understanding of ethical and societal concepts that are important to computer professionals, and experience of considering ethical dilemmas.

15 credits

Team Project

The Team Project provides the opportunity for you 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. You will form groups and choose a project which interests you, 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. You will present the project overview within the first few weeks of the semester and submit a report on the work at the end of semester.

15 credits

Data Science with Python

This module starts with a rapid review of basic background mathematics and statistics, and an introduction to Python. The module will then introduce students to a range of statistical and programming techniques and give practice in their implementation and interpretation using Python. 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, statistical data modelling, programming and optimization. Students will also learn the fundamentals of robust data management and reproducible scientific analysis.

15 credits

Artificial Intelligence Dissertation Project

For your individual project, you 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 you will have a personal academic supervisor to guide you during this period.

60 credits

Optional modules:

Modelling and Simulation of Natural Systems

This module will provide a practical introduction to techniques used for modelling and simulating dynamic natural systems. Many natural systems can be modelled appropriately using differential equations, or individual based methods. In this module, you will explore and understand both modelling approaches. You will gain knowledge of the assumptions underlying these models, their limitations, and how they are derived. You will learn how to simulate and explore the dynamics of computational models, using a variety of examples mostly drawn from natural systems.  At the end of the module, we will introduce basic recurrent neural networks as examples of dynamical systems with multiple timescales. You should be aware that there are limited places available on this course.

15 credits

Computer Security and Forensics

This module provides an introduction into computer security and forensics focussing on approaches and techniques for building secure systems and for the secure operation of systems. It aims to develop knowledge and understanding of fundamental principles of information security, develop familiarity with compromise of computer systems and what countermeasures can be adopted and provide practical experience of implementing secure 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) 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. Students should be aware that there are limited places available on this course.

15 credits

Network Performance Analysis

This module considers the performance of computer networks from a statistical aspect, using queuing theory. It is shown that the performance of a computer network depends heavily on the traffic flow in the network, and different models of traffic and queues are used. These include single-server queues, multiple server queues, and the concept of blocking is discussed. Although the analysis is entirely statistical, all the relevant background is provided in the lectures, such that the course is entirely self-contained. Problem sheets are provided in order to assist you with the course material. You should be aware that there are limited places available on this module.

15 credits

Parallel Computing with Graphical Processing Units (GPUs)

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 central processing units (CPUs) to graphics processing unit (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. 

15 credits