The course focuses on the quantitative genetics and statistical background of models to predict traits based on genomic information (genomic prediction), with main focus on agricultural (plant and animal breeding) and some related biological applications. The course addresses prediction from high-dimensional data covering tools like mixed models, Bayesian shrinkage approaches and machine learning kernel methods and discusses use of kernel methods to handle interactions and non-linear regressions. All approached are trained in computer practicals with the objective that students obtain an understanding of the statistical principles of the different models, and can analyse data with a critical assessment of the results from different statistical approaches.
Exam info and full course description can be found in the course catalogue.
A relevant bachelor's degree within Science and Technology.
Exchange Students: nomination from your home university
Freemovers: documentation for English Language proficiency
You can read more about the admission here.