Topic: hierarchical models

Learn regression models that estimate nonlinear relationships from data using splines. Starting from generalised linear models, the course develops fitting and visualisation with mgcv and gratia, explains how smoothing controls overfitting, and covers model checking, selection and inference. Further exercises introduce interacting smooths, hierarchical models with group and individual effects, and posterior simulation, using ecological and environmental examples. Sessions: 7–10 December, 14:00–20:00 daily, Europe/Berlin (CET, UTC+01:00). Live online training; fee €480. R/RStudio fluency is r

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.