Computer Vision workshops

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). Online course; fee €530. A biology background is expected; basic Python and Linux familiarity is helpful but not required.

This practical workshop explains how to establish chronic video-EEG recordings in rodent epilepsy models, from equipment and electrode placement to seizure monitoring and data management. Analysis ranges from spike and seizure quantification to machine learning. A second component introduces Motion Sequencing (MoSeq), an unbiased method for identifying and measuring behavior from video, with workflows for studying seizures, behavioral changes and associated conditions. Participants examine the hardware, software and implementation choices needed to introduce both approaches into their laboratories. Monday 7 December, 10:00–11:30 Mountain Standard Time (America/Denver). Part of the 2026 American Epilepsy Society Annual Meeting. In-person meeting registration and a free myAES account are required. Standard full-meeting rates are USD 800 for member scientists and USD 1,075 for non-member scientists; student rates are USD 250 and USD 360, respectively. Other professional and one-day categories are available. Seating priority goes to registered attendees.

This hands-on course trains microscopy image-analysis practitioners to apply deep learning to their own data and research problems. It addresses the gap between successful neural-network methods and the practical difficulties of training and deploying them. Applications span image restoration, classification and segmentation, object detection and morphological characterization, high-throughput imaging, and large-scale three-dimensional processing. The course focuses on practical use of these methods for microscopy rather than treating model development as a purely theoretical subject.