School of Computer Science and Statistics
(Semester 2; 5 ECTS) This module will include an overview of classical and modern time series methods; Exponential smoothing and Holt Winters; ARIMA models; using transformations; implementing and validating times series models in a variety of contexts. An emphasis will be placed on learning how to implement these techniques using R or other statistical software.
(Semester 2, 5 ECTS) This module is designed to introduce Sustainable Development to first year Computer Science students and address sustainability themes and key competencies, with a focus on ICT.
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 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
Module Code CS7092 Module Name Dissertation ECTS Weighting [1] 30 ECTS Semester Taught Semester 2 Module Coordinator/s Assigned Supervisor Module Learning Outcomes On successful completion of the project, the … Read more
(Semester 2; 5 ECTS) This module will include the theory of multivariate distributions, including the multivariate Gaussian distribution.; methods for dimension reduction; classification methods and clustering techniques.
(Semester 2, 5 ECTS) This module provides a holistic overview of how a typical general purpose computing system functions, followed by a deep-dive into the various key architectural features of such systems.
(Semester 2, 5 ECTS) This module continues directly from CSU11021 and examines the structure and behavior of computer systems in greater depth. In particular, this module introduces students to the implementation of simple data structures (stacks, multi dimensional arrays, composite data types), subroutines (including parameter passing conventions), exceptions, interrupts and basic I/O at the machine level.
(Semester 2; 5 ECTS) In this module, students combine statistics and sustainability knowledge to work on multiple group projects.
Module Code STP80080 Module Name Foundations of Data Science 1 ECTS Weighting [1] 5 ECTS Semester taught Semester 2 Module Coordinator/s Prof. Simon Wilson Module Learning Outcomes On successful completion … Read more