FTE-35306 Machine Learning


Credits 6.00

Teaching methodContact hours
Course coordinator(s)dr. GW Kootstra
Lecturer(s)prof. dr. ir. D de Ridder
dr. ir. S van Mourik
dr. GW Kootstra
prof. dr. D Tuia
dr. ADJ van Dijk
H khan
Examiner(s)prof. dr. ir. D de Ridder
dr. ir. S van Mourik
prof. dr. D Tuia
dr. ADJ van Dijk
dr. GW Kootstra

Language of instruction:


Assumed knowledge on:

mathematics (Mathematics 1; MAT-14803 and Mathematics 2; MAT-14903, or equivalent), statistics (Data Analysis Biosystems Engineering; FTE-26306, Advanced Statistics; MAT-20306, or equivalent), and familiarity with computer programming (e.g. Programming in Python; INF-22306).


Due to the limited teaching staff available this course has a maximum of 80 students.  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, including biosystems engineering and bioinformatics. This course discusses regression, classification, clustering, and supporting methods.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- clearly explain machine learning problems, algorithms, and their formulas;

- understand well how machine learning can be used in a) biosystems engineering (MAB), 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.


Interactive lectures, self-study, pen-and-paper exercises, computer exercises, and project work.


The final grade will be determined by your project grade (50%, based on the quality of the submitted project code and report) and your written exam grade (50%, based on the quality of your written exam). For both, a minimum grade of 5.5 is required. If your written exam grade is below 5.5, you can try to improve it during one of the re-exam opportunities. If your project grade is below 5.5, you can submit a new version in the same week as one of the re-exam opportunities. The written exam is not an open book exam, but you will be provided with a formula sheet. During the 4 project days, you work in pairs. Your performance directly impacts the grade of your partner, as you will both obtain the same grade. On these 4 project days, attendance is obligatory. At the end of each project day, 2 pairs will give a short presentation.


James G, Witten D, Hastie T, Tibshirani R: An introduction to statistical learning (ISBN978-1-4614-7138-7, freely available online), supported by various other sources.

Restricted Optional for: MEEEarth and EnvironmentMSc4WD
MBEBiosystems EngineeringMSc4WD
MBEBiosystems EngineeringMSc4WD
MGIGeo-Information ScienceMSc4WD