Chris will give an brief overview of the basic principles of machine learning (including the selection of appropriate applications for machine-learning driven models in the clinical context), and cover the methods that are most commonly used in application to clinical data - including neural networks, tree-based models and reinforcement learning. To illustrate the use of each of the these approaches Chris will describe current projects that are using each method to produce clinically useful outputs.

Learning outcomes:

• Understanding of basic principles of machine learning.
• Understanding of how these principles can be applied to clinical scenarios and decision making.