Aims:

To 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 outcomes:

On successful completion of the module, a student will be able to:

Understand the 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.
	Handle real-world data in computer programs.
Content:

The module introduces the basics of mathematical modelling: the distinction between models and the real world; when and how models are useful; advantages and disadvantages of explicit model-based approaches.

The module covers a range of model based approaches to biomedical imaging and image analysis and basic computer science techniques that underpin them. The intention is to introduce the students to standard techniques of parameter estimation in a hands-on practical way within the context of model-based imaging and image-based modelling.

The module also gives exposure to common applications and challenges in biomedical imaging. It uses several example applications (including microstructural MRI and disease progression modeling) to introduce different kinds of model and, more fundamentally, new algorithms and techniques for parameter estimation, optimization, sampling and validation.

Requisites:

In order to be eligible to select this module as an option or elective, a student must be registered on a programme for which it is a formally approved option or elective choice.

The module makes heavy use of Matlab programming for courseworks, although a strong programmer in other languages will pick up the necessary Matlab during the course. It also assumes a strong grasp of general engineering mathematical concepts, in particular linear algebra (intermediate), probability and statistics (intermediate), geometry, and calculus.

Students familiar with statistical modeling, parameter estimation, and machine learning will pick up the content fairly easily; those less familiar with such concepts sometimes find the workload heavy. To get an idea of the content, have a look at section IV (Probabilities and Inference) of the Mackay Information Theory, Inference and Learning Algorithms book: Mackay Information Theory, Inference and Learning Algorithms book.