Machine Learning

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.

Note: there is a significant overlap with the course Statistics for Data Scientists (MAT32806). We recommend students to not take both courses. Machine Learning is recommended if you intend to take the courses Deep Learning (GRS34806) and/or Advanced Machine Learning (FTE36806).

Assumed Knowledge:

• Mathematics (Mathematics 1 (MAT14803) and Mathematics 2 (MAT14903), or equivalent);
• Statistics (Data Analysis Biosystems Engineering (FTE26306), Advanced Statistics (MAT20306), or equivalent);
• Programming in Python (INF22306).

https://wur.osiris-student.nl/onderwijscatalogus/extern/cursus?cursusid=819441&taal=en