Module description
The module will provide the students with a fundamental grounding in the theoretical and computational skills required to apply machine learning tools to real-world problems. It will provide an understanding of the application of these skills to explore complex high-dimensional data sets; providing an overview of active research areas in machine learning, with biomedical applications.
Assessment details
| Written Exam |
50% |
| Coursework 1 |
25% |
| Coursework 2 |
25% |
Semester 1-only Study abroad students will be set an alternative assessment to the January exam.100% Coursework
Educational aims & objectives
The module will provide students with a grounding in fundamental machine learning topics as well as practical examples of how to apply these techniques to real world data. Topics will include the foundations of machine learning, linear classification and regression, Support Vector Machines, Penalised Regression, Dimensionality Reduction and Manifold Learning, as well as ensemble learning (Random Forests, Bagging and Boosting) and advanced image segmentation techniques.
Learning outcomes
On completion of the course the students should:
- have acquired a basic understanding of the most fundamental concepts related to machine learning.
- understand and apply a range of commonly used machine learning techniques.
- have acquired practical computational skills that are needed to manipulate complex datasets.
- be able to apply techniques learnt to domains related to biomedical applications.