Module Aims

This module aims to link the fundamental concepts presented in “Introduction to Machine Learning” to practical examples frequently encountered in Health Data Science and, in parallel, introduce some new methods and advanced elements of previously discussed canonical methods. 

Module Learning Outcomes

By the end of the course, students should be able to: 

1. Describe and evaluate several canonical machine learning methods and feature selection processes (including assumptions, algorithms and examples) 
2. Contextualise machine learning methodology into the broader statistical methodology.
3. Identify the most appropriate machine-learning methods (and critically compare their performance and the stability of results obtained using standard approaches) to solve a range of inferential and prediction problems 
4. Use standard Python packages to fit machine learning models.
5. Interpret the output of machine learning algorithms in the context of the underlying modelling assumptions.

 Pre-requisites

• Statistics for HDS
• Introduction to Machine Learning
• Advanced Biostatistics for HDS

Teaching Strategy

Lectures and computer practicals. Some preliminary reading may be required. 

Assessment

A practical assessment on a dataset similar to the ones employed during the course.

 Module Length

4 days