FTE-35306 Machine Learning


Credits 6.00

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

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 some familiarity with computer programming (e.g. Programming in Python; INF-22306)


Pattern recognition deals with algorithms that predict certain outputs (such as crop yields or traits) given previously unseen input data from sensors, cameras, maps, molecular measurements etc. These predictors learn how to do so using training data (sets of input examples, usually with corresponding outputs). Pattern recognition 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 competion of this course students are expected to:
- clearly explain machine learning problems, algorithms, and their formulas;
- understand well how machine learning can be used in
- biosystems engineering (MAB: data mining, precision farming, decision support systems, computer vision, agricultural robotics)
- bioinformatics (MBF: data mining, molecular diagnostics, function inference, interaction prediction), 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: MBEBiosystems EngineeringMSc4WD