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 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) 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) In this module, students will explore the prevailing vision for an Internet of Things in
a practical, pragmatic manner.
(Semester 2, 5 ECTS) Appreciate the scope, applications and limitations of artificial intelligence;
(Semester 2, 5 ECTS) This module continues on from CS7CS4 (Machine Learning) with a focus on sampling methods and topical applications.
(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.