School of Computer Science and Statistics
(Semester 2, 5 ECTS) This module focuses on practical application of machine learning techniques to radio and optical transmission networks. It will start with an overview of the machine learning techniques that are applicable to some specific problems in the networking domain and then provide deeper insight into those that will be used in the lab to address the specific use cases described below
(Semester 2, 5 ECTS) This course takes a critical look at some of the architectural issues involved in, and paradigms available for, the construction of large-scale distributed systems such as the infrastructures supporting Google’s search engine or Amazon’s online sales platform. In particular, the course considers how to develop systems that must make trade-offs between performance, consistency, reliability, and availability.
(Semester 1, 5 ECTS) The module is designed to explore the management, delivery and inter-operability of knowledge, information and data through knowledge and data engineering.
(Semester 1, 5 ECTS) An introduction to computer graphics; problem domain and applications.
(Semester 1, 5 ECTS) Image processing, feature detection and matching, image registration, recognition
and segmentation – Motion flow and object tracking in video – Mathematics for
computer vision.
(Semester 2, 5 ECTS) This module deals with programming for GPU pipeline architectures e.g. geometry,
rasterisation, texturing, fragment / pixel and vertex shaders.
(Semester 2, 5 ECTS) User modelling, including Task modelling
User preferences
(Semester 2, 5 ECTS) Grasp the scope and limitations of finite state methods in text analysis.
(Semester 2, 5 ECTS)
This course covers fundamentals and state-of-the-art in augmented reality, as well
as related areas of 3D computer vision and graphics.
(Semester 1, 5 ECTS) This module aims to provide both a theoretical and practical understanding of urban
computing and associated cyber-physical concepts, principles, challenges and
solutions.