Topic: cross-validation

Build and evaluate deep-learning models for biological prediction using Python and Jupyter notebooks. Lectures and collaborative exercises develop convolutional networks for classification, regression and image segmentation, with attention to preprocessing, augmentation, class imbalance, cross-validation and overfitting. Later sessions explore learning-rate and training-duration choices, recurrent networks and applications to omics data. Participants interpret model performance and discuss their own research problems. 2026-11-23–2026-11-27, 14:00–20:00 daily, Europe/Berlin (CET, UTC+01:00). O

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

A one-year machine-learning research internship with the Allen Institute Neural Dynamics accelerator. The intern will analyze recordings from two genetically distinct neural populations in the striatum alongside motor output, muscle activity and behavioral video. The project asks how shared and population-specific neural activity encodes movement, using latent-variable models, regression, classification and cross-validation. Work includes reproducible Python analysis and communicating results through figures and methods that may contribute to a poster or publication. No previous neuroscience b

Restoring hand function is a top priority for individuals with tetraplegia. This challenge motivates considerable research on brain-computer interfaces (BCIs), which bypass damaged neural pathways to control paralyzed or prosthetic limbs. Here, we demonstrate the BCI control of a prosthetic hand using intracortical recordings from the posterior parietal cortex (PPC). As part of an ongoing clinical trial, two participants with cervical spinal cord injury were each implanted with a 96-channel array in the left PPC. Across four sessions each, we recorded neural activity while they attempted to pr

Conventional approaches to hypothesis testing often hinge on linear models like ANOVA, subjective decision-making regarding factors and their interactions, and reliance on in-sample inference without thorough evaluation on hold-out data. These oversimplifications, subjective choices, and limited generalizability may cast doubt on the relevance and validity of scientific theories and conclusions. A transformative shift towards more resilient methodologies entails the incorporation of interpretability techniques, sophisticated machine learning models, and advanced out-of-sample model evaluation

We present a stochastic dynamics theory and analysis framework for studying a continuous time transparent visual game for pairs of agents we call continuous perceptual report (CPR) tasks.The CPR task is a pattern recognition task where subjects view a moving random dot pattern with a time-dependent veridical direction of motion and variable coherence. The task is transparent in that each subject receives information about the current estimate of the other. The difficulty of this type of task depends on the coherence and noise level of the stimulus, on the accessibility of information from the