Machine Learning deals with algorithms that predict certain outputs (such as crop yields or traits) given previously unseen input data from cameras, other sensors, maps, molecular measurements etc. These algorithms learn how to do so using training data (sets of input examples, usually with corresponding outputs). Machine learning plays an increasingly important role in many scientific areas.
This course discusses the theory of different methods for regression, classification, and clustering and the application thereof in different fields of agricultural and life sciences. Students will learn how to properly train and evaluate machine-learning models on data, what typical issues are that can arise, and how to deal with these. Furthermore, attention is paid to the ethical, legal, and social aspects of applying machine learning in practical use-cases.
During the course, every day is dedicated to a specific topic, with a lecture and pen-and-paper exercises in the morning, and a practical session with computer exercises in the afternoon. In addition, there are four project days, where students work in pairs to create a solution for practical use-cases.