Topic: decision trees

Learn to build and evaluate machine-learning workflows in R with tidymodels and tidyverse. The workshop moves from problem definition, dataset splitting and preprocessing to decision trees, random forests and gradient boosting. Practical work covers cross-validation, hyperparameter tuning, regression and clustering, with model-building demonstrations, live coding, quizzes and individual or group exercises. Participants should already be comfortable with R, basic data analysis and statistics; machine-learning experience is not required. Sessions on 8, 15, 22 and 29 January 2027, 16:00–19:30 Ma

AIMS: Sharp-wave ripples (SWR) are high frequency events recorded in the local field potential (LFP) of the hippocampus of rodents and humans. During neurological conditions such as epilepsy and Alzheimer’s disease, changes on the waveforms of SWRs are considered as biomarkers of dysfunction. However, SWR features cannot be fully characterized by spectral methods alone.METHODS: Here, we introduce a machine learning (ML) toolbox resulting from a community-based hackathon, that operates over high-density LFP recordings to detect hippocampal SWR. We train a wide variety of ML architectures (suppo