All my current teaching is on the MSc Health Informatics with Data Science at the University of Leeds. I am responsible for two modules on the use of statistics and AI with healthcare data (details below).
This module introduces you to statistical testing, generalised linear models (GLMs) and survival models, which are the foundation for analysing observational healthcare data. By the end of the course, you'll be able to model various healthcare outcomes of interest on real-life datasets including 30-day mortality, treatment costs, length of stay in hospital, from NHS digital etc. The module will also convey best practice in model evaluation and validation, based on the TRIPOD and STAR-D guidelines for reporting of statistical models in medical journals.
This module will introduce you to a variety of different machine learning algorithms for supervised and unsupervised learning problems. These include random forest, support vector machines, k-means clustering, and neural networks with use-cases identified from across the healthcare domain. You'll also be introduced to techniques for feature selection, dimensionality reduction, and in avoiding overfitting. This builds upon knowledge gained in the core module on statistical modelling. By the end of the module, you'll be familiar with a variety of alternative approaches to traditional statistical modelling and will have gained experience in using them within Python.