Apply modern machine learning methods to marine mammal datasets in our live online course *Machine Learning for Ecological Time Series (METR01)*. This applied R course teaches ecologists how to analyse, model, and predict ecological time-series data using practical, reproducible workflows. The methods are highly relevant to marine mammal research, where long-term monitoring, telemetry, acoustic detections, and environmental covariates generate complex time-series datasets that require robust analytical approaches. Examples of relevant applications include: - Analysing long-term abundance or sighting records - Modelling behavioural state changes from tagging or biologging data - Forecasting habitat use or distribution shifts under environmental change - Detecting patterns in passive acoustic monitoring time series - Integrating environmental covariates with movement or population data The course covers: - Preparing ecological time-series datasets for analysis - Supervised and unsupervised machine learning methods - Model validation, forecasting, and interpretation - Building reproducible workflows in R Delivered online with recordings available afterwards, participants receive course materials, example datasets, and post-course support. *Course details* Dates: 13–17 April 2026 Duration: 5 days (approximately 7 hours per day) Format: Recorded sessions with live Q&A Fee: £450 This course is ideal for marine mammal researchers working with telemetry, acoustic monitoring, survey time series, or environmental datasets who want practical skills for analysing complex ecological data using machine learning. Full details and registration: https://prstats.org/course/machine-learning-for-ecological-time-series-metr0... Email oliver@prstats.org with any questions -- Oliver Hooker PhD. PR stats