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The new minor offering from Leiden, Delft and Erasmus will be visible in early March.

Machine Learning

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).

Learning outcomes

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.

Teaching method and examination

The final grade will be determined by

  • your project grade based on the quality of the submitted project code and report (in pairs, 50%), and;
  • your grade for the closed-book written exam with a combination of closed and open questions, with a formula sheet provided (50%).

For both, a minimum grade of 5.5 is required.
If your written exam grade is below 5.5, you can take a resit during one of the re-exam periods.
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. A sufficient project grade will remain valid for two years.

Required prior knowledge

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).

Link to more information

If anything remains unclear, please check the FAQ of Wageningen University.

Offering(s)

  • Start date

    12 februari 2024

    • Ends
      8 maart 2024
    • Term *
      Period 4
    • Location
    • Instruction language
      English
    Enrolment period closed
These offerings are valid for students of Utrecht University