School of Computer Science

Module 06-22313 (2013)

Computational Tools for Modelling and Analysis

Level 4/M

Iain Styles Semester 2 10 credits
Co-ordinator: Iain Styles
Reviewer: Shan He

The Module Description is a strict subset of this Syllabus Page.


The module introduces concepts, techniques and tools for the computational modelling and analysis of image-derived data. The key schemes for data representation, and a survey of state-of-the art methods for statistical data analysis, stochastic modelling, machine learning and optimisation, will be presented in context of imaging processes (e.g. stochastic nature of the photon-matter interaction; optimisation of parameters of an imaging system) and image-derived data (e.g. factor analysis, Markov Chain modelling, cluster analysis, pattern recognition to identify "hidden" patterns in data). The individual topics will be presented by leading researchers from Computer Science. The module will also provide practical experience of using a subset of the techniques, implemented in Matlab. These laboratory/exercise class sessions necessitate and give rise to the higher formal contact hours for this module when compared to some other PSIBS modules.

Learning Outcomes

On successful completion of this module, the student should be able to:

  1. Explain the principles of the main methods for statistical data analysis, stochastic modelling, machine learning and optimisation and explain (and communicate) their advantages and limitations
  2. Identify, justify and apply suitable analysis, modelling and optimisation techniques to image-derived data originating from biomedical research problems 3.Implement solutions in Matlab and critically appraise which analysis approaches should be used for a given kinds of problems

Teaching methods

Lectures, seminars, laboratory / exercise classes

Contact Hours:



Sessional: Portfolio of exercise/laboratory reports: 30%. Structured written unseen examination of 1.5 hours organised by School: 70%.

Detailed Syllabus

Not applicable