Machine Learning for Computational Cancer Genomics

Researchers learn the assumptions and implementation principles behind machine-learning models used in cancer genomics. The five-day programme develops hidden Markov approaches for copy-number inference, mixture models for tumour subclones, hierarchical models for mutation signals and autoencoders for single-cell data. Lectures alternate with discussion, coding practice and a final project, emphasizing inference of latent biological states from noisy or incomplete measurements. Participants may discuss suitable datasets they own. Online teaching runs 14:00–19:00, Monday through Friday.