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Machine learning plays an increasingly important role in many scientific areas, including geo-information science and remote sensing, ecology, biosystems engineering and bioinformatics. Today, scientific data are growing in complexity, size, and resolution, and scientists are challenged to leverage available data to inform decision making. In this course, you will learn how to model patterns and structures contained in data, and evaluate data-driven models, i.e. models that learn directly from observations the phenomena under study.
The course will focus on the following topics:
Through a series of lectures and practical exercises (in R), the participants will learn about different strategies and their pertinence for specific problems in environmental sciences, but the course will remain general for a broader audience. Participants are encouraged to bring their own problems in class and analyse data from their own research.
|Course Frequency||To be determined|
|Prior knowledge||Basic skills in statistics are a plus. Practicals will be in R. A short introduction will be provided on the first day, but previous programming experience in R or Python is required|
|Intended credits||1.5 ECTS|
|Course organisation||The C.T. de Wit Graduate School for Production Ecology and Resource Conservation (PE&RC)|
|More information||Course website|