Machine learning for biostatistics

Recent years have brought a rapid growth in the amount and complexity of health data captured. Among others, data collected in imaging, genomic, health registries and personal devices call for new statistical techniques in both predictive and descriptive learning. Machine learning algorithms for classification and prediction complement classical statistical tools in the analysis of these data. This unit will cover modern machine learning methods particularly useful for large and complex data. Topics include, classification trees, random forests, model selection, lasso, bootstrapping, cross-validation, generalised additive modelling, and regression splines. The statistical software R package will be used throughout the unit.