COMPGV17 - Computational Modelling for Biomedical Imaging

This database contains the 2016-17 versions of syllabuses. Syllabuses from the 2015-16 session are available here.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

CodeCOMPGV17 (also taught as COMPM077)
Taught ByDanny Alexander, Ivana Drobnjak, Gary Zhang (100%)
AimsTo expose students to the challenges and potential of computational modelling in a key application area. To explain how to use models to learn about the world. To teach parameter estimation techniques through practical examples. To familiarize students with handling real data sets.
Learning OutcomesStudents successfully completing this module should be able to:
  • Understand tha aims of biomedical imaging
  • understand the advantages and limitations of model-based approaches and data-driven approaches
  • Have knowledge of standard techniques in modelling, experimental design and parameter estimation.
  • Understand the challenges of data modelling, experiment design and parameter estimation in practical situations
  • gain knowledge of handling real-world data in computer programs.