Machine Learning for Ecological Time Series (METR01) <https://prstats.org/course/machine-learning-for-ecological-time-series-metr01/> https://prstats.org/course/machine-learning-for-ecological-time-series-metr01/
Machine Learning for Ecological Time Series is an applied R course teaching ecologists how to analyse, model, and predict ecological time series data. Time series data are central to ecological and environmental research, from monitoring population dynamics and climate signals to tracking ecosystem responses to change. This intensive course provides a practical and conceptually grounded introduction to state-of-the-art machine learning approaches for analysing ecological time series, with a strong emphasis on real-world application and best practice. Delivered by experienced instructors, the course blends theory, hands-on coding, and applied examples to help participants confidently apply machine learning techniques to their own data. ------------------------------ Who Is This Course For? This course is ideal for: - PhD and MSc students - Postdoctoral researchers - Principal investigators - Environmental and ecological professionals Participants should have some prior experience with *Python*. Core concepts will be reviewed at the start of the course, but a basic familiarity with the language will help you get the most out of the practical sessions. ------------------------------ What You Will Learn By the end of the course, you will be able to: - Understand the strengths and limitations of machine learning for time series analysis - Prepare, explore, and preprocess ecological time series data - Apply supervised and unsupervised machine learning methods to temporal data - Build, validate, and interpret predictive models - Avoid common pitfalls such as overfitting, data leakage, and improper validation - Critically assess when machine learning is (and is not) appropriate for ecological inference ------------------------------ Course Highlights - *Ecology-focused examples*: Methods are taught using realistic ecological and environmental datasets - *Hands-on learning*: Practical coding exercises throughout, with reusable workflows - *Best-practice modelling*: Emphasis on reproducibility, validation strategies, and interpretability - *Expert support*: Live Q&A sessions with instructors to discuss concepts and troubleshoot code - *Flexible delivery*: Recorded lectures combined with live sessions - *Certificate of completion*: Digital certificate provided at the end of the course ------------------------------ Course Format - *5-day online course* - Pre-recorded lectures released daily - Live Q&A sessions for discussion and clarification - Continued access to course materials and recordings after completion ------------------------------ Why Take This Course? Machine learning is increasingly used in ecological research, but applying these methods responsibly requires more than simply fitting models. This course equips you with both the conceptual understanding and practical skills needed to use machine learning effectively, transparently, and confidently in ecological time series analysis. *Harness modern machine learning methods to analyse, model, and interpret ecological time series data - Book today!* *Email [email protected] <[email protected]> with any questions* *You may also be interested in * Machine Learning for Time Series (MLTP01) <https://prstats.org/course/machine-learning-for-time-series-mltp01/> https://prstats.org/course/machine-learning-for-time-series-mltp01/ Machine Learning for Time Series is a practical Python course teaching how to model, analyse, and forecast time series data using machine learning methods. -- Oliver Hooker PhD. PR stats [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
