An introduction to machine learning for health research


In this one-day short course, we will provide a broad grounding in machine learning basics, illustrated throughout by applications in biohealth. Methodological topics will include: (i) Modelling, generalisation, and overfitting; (ii) An introduction to regression and classification; (iii) Unsupervised learning: clustering, density estimation, and dimension reduction; and (iv) Advanced predictive modelling: artificial neural networks and Gaussian processes. Topics will be accompanied throughout by practical illustrations and applications in R.

Presenters: Paul Kirk is an MRC Investigator at the MRC Biostatistics Unit, University of Cambridge, whose research group uses Bayesian statistics and machine learning to address questions in molecular precision medicine and to identify patterns in electronic health record databases.