About this course
This course is a hands-on course for modelling in a wide agricultural context. Besides the basic data-handling skills, this course especially focuses on the creativity-based aspects of modelling. Here, inspiration from modellers from industry and academia is provided to stimulate the develop of models. Modelling will be carried out individually, in groups and with a tutor to come up with (creative) solutions. The starting point of this course is either a raw dataset (data-driven modelling), a research question (knowledge-based modelling), or a combination of both approaches, and is always connected to a real-life problem. The aim is experience the whole modelling workflow, and to build/develop a more complex analyses throughout the course.
Learning outcomes
Explain the role and key principles of data-driven and knowledge-based modelling in agriculture
Explain how both modelling approaches can be integrated and why that is needed
Infer the opportunities and challenges of data-driven and knowledge-based modelling
Apply basics of data analytics (quality of data, pre-processing)
Interpret and translate complex descriptions of biological systems into mathematical models
Design and evaluate code for data-driven and knowledge-based models, separately or in combination
Calibrate and validate data-driven and knowledge-based models
Assessment method
- Assignment other (80%) If the final grade is not sufficient (< 5.5), all group members will get an individual assignment as a resit opportunity
- Written test with closed questions (20%)
Prior knowledge
PPS20306 Systems Analysis and Modelling;
INF22306 Programming in Python.
Understanding basic plant science concepts, as well as basics in Python, are necessary to follow this course.
Resources
- Will be made available via Brightspace.
Additional information
- Contact a coordinator
- Levelmaster
- Mode of instructionon campus