Integrated Computer Science
Year 5 (Master in Computer Science)
Students select to take the integrated Master in Computer Science (MCS) programme during their Senior Sophister year (typically the deadline is early in October), and as part of this programme spend the second half of Year 4 on an Internship programme. Details of how to apply for this programme are on the Senior Sophister page.
All Year 5 students take a Research Methods module and undertake a significant dissertation project (30 credits). They also select five elective modules from the list below counting for 25 credits some of which are run in Michaelmas term and some in Hilary term.
Module CodeCS7092Module NameDissertationECTS Weighting30 ECTSSemester taughtSemester 2Module Coordinator/s Assigned Supervisor Module Learning Outcomes On successful completion of the project, the students will be able to: derive, apply and adapt solutions…
(Semester 1, 5 ECTS) Locate, obtain and critique relevant knowledge and evidence to support innovation and research
(Semester 1, 5 ECTS) Understand what machine learning is and how it works.
(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.
(Semester 2, 5 ECTS) This module continues on from CS7CS4 (Machine Learning) with a focus on sampling methods and topical applications.
(Semester 1, ECTS 5) This module aims to equip the student with the knowledge and tools to visualise data in ways that give insight and understanding.
(Semester 1, 5 ECTS) Image processing, feature detection and matching, image registration, recognition
and segmentation – Motion flow and object tracking in video – Mathematics for
(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.
(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)
This course covers fundamentals and state-of-the-art in augmented reality, as well
as related areas of 3D computer vision and graphics.
(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.
(Semester 1, 5 ECTS) An introduction to computer graphics; problem domain and applications.
(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 2, 5 ECTS) Appreciate the scope, applications and limitations of artificial intelligence;
(Semester 1, 5 ECTS) Explain the process of content indexing in information retrieval including stop word removal, conflation (stemming, string-comparison), and the language dependency of these methods.
(Semester 2, 5 ECTS) Grasp the scope and limitations of finite state methods in text analysis.
(Semester 2, 5 ECTS) User modelling, including Task modelling
(Semester 1, 5 ECTS) This module aims to provide a theoretical and practical understanding of modern scalable systems and architectures, from billions of highly distributed Internet of Things devices, through to present day concepts, such as Cloud architectures and systems.
(Semester 2, 5 ECTS) In this module, students will explore the prevailing vision for an Internet of Things in
a practical, pragmatic manner.
(Semester 1, 5 ECTS) This module aims to provide both a theoretical and practical understanding of
modern and next generation networking and systems concepts, principles, practices
and technologies. Contemporary and emerging wired and wireless network systems
(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
(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.
(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.