About this course
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 course Deep Learning (GRS34806) and required if you intend to take the course Advanced Machine Learning (FTE36806).
After successful completion of this course students are expected to be able to:
- explain machine-learning problems, algorithms, and their formulas;
- apply machine-learning techniques in a) biosystems engineering (MBE), b) bioinformatics (MBF), c) geo-information sciences and remote sensing (MGI), or another field of study;
- qualitatively and quantitatively compare the characteristics, (dis)advantages, formulas, and performance of a number of key algorithms;
- design and implement effective solutions based on chosen algorithms, to solve practical problems.
- 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).