Augmented and Virtual Reality – Core Modules
CS7CS3 – Advanced Software Engineering
(Semester 1 & 2, 10 ECTS) Assess the theory of classic architecture principles and apply an appropriate architectural model in a team-based application under development
CS7CS4 – Machine Learning
(Semester 1, 5 ECTS) Understand what machine learning is and how it works.
CS7CS6 – Research and Innovation Methods
(Semester 1, 5 ECTS) Locate, obtain and critique relevant knowledge and evidence to support innovation and research
CS7CS5 – Dissertation
(Semester 3, 30 ECTS) Engage in a sustained piece of individual, academic research on a
chosen topic within the field of computer science.
CS7GV5 – Real-Time Animation
(Semester 2, 5 ECTS) The aim of this module is to provide students with a deep understanding of the theory and techniques behind real time animation.
CS7GV2 – Mathematics of Light and Sound
(Semester 1, 5 ECTS) Wave equation and its solution; Maxwell´s equations; Fourier transform and analysis; vibration; mass-spring-damper systems; numerical methods; simulation software.
CSP7GV7- Extended Reality
(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.
CS7GV3 – Real-Time Rendering
(Semester 2, 5 ECTS) This module deals with programming for GPU pipeline architectures e.g. geometry,
rasterisation, texturing, fragment / pixel and vertex shaders.
CS7GV1 – Computer Vision
(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.
CS7GV6 – Computer Graphics
(Semester 1, 5 ECTS) An introduction to computer graphics; problem domain and applications.
Augmented and Virtual Reality – Elective Modules
CS7DS2 – Optimisation Algorithms for Data Analysis
(Semester 2, 5 ECTS) The aims of this module are to give the student skills to model, analyse and solve optimisation problems that arise in data analytics and modern computing and communication systems.
CS7DS3 – Applied Statistical Modelling
(Semester 2, 5 ECTS) This module continues on from CS7CS4 (Machine Learning) with a focus on sampling methods and topical applications.
CS7IS2 – Artificial Intelligence
(Semester 2, 5 ECTS) Appreciate the scope, applications and limitations of artificial intelligence;
CS7NS2 – Internet of Things
(Semester 2, 5 ECTS) In this module, students will explore the prevailing vision for an Internet of Things in
a practical, pragmatic manner.
CS7NS5 – Security and Privacy
(Semester 2, 5 ECTS) The objectives of this module are: to develop an in-depth understanding of risk, data
privacy, threats and risks of security breaches, an awareness of computer security
(cryptographic) and protocol techniques, and an ability to make appropriate
decisions about securing data.
CS7IS4 – Text Analytics
(Semester 2, 5 ECTS) Grasp the scope and limitations of finite state methods in text analysis.
CS7IS5 – Adaptive Applications
(Semester 2, 5 ECTS) User modelling, including Task modelling
User preferences
CS7NS6 – Distributed Systems
(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.
CSP55040 – Machine Learning Application to Radio and Optical Networks
(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