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:
- The machine learning methodology, and framing scientific problems as machine learning tasks
- Data preparation and representation
- Key algorithms for regression, classification, and clustering
- Qualitative and quantitative comparison of characteristics, (dis)advantages, and performance of a number of key algorithms
- Design and implementation of effective solutions based on chosen algorithms to solve practical problems
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.