GRS-34806 Deep Learning


Credits 6.00

Teaching methodContact hours
Group work3
Independent study0
Course coordinator(s)prof. dr. D Tuia
dr. I Athanasiadis
Lecturer(s)prof. dr. ir. D de Ridder
dr. GW Kootstra
dr. ADJ van Dijk
prof. dr. D Tuia
dr. I Athanasiadis
Examiner(s)dr. GW Kootstra
prof. dr. ir. D de Ridder
prof. dr. D Tuia
dr. I Athanasiadis
dr. ADJ van Dijk

Language of instruction:


Assumed knowledge on:

FTE-35306 Machine learning and one Python programming course (e.g. INF-22306 programming in python or GRS-33806 Geoscripting or BIF-50806 Practical computing for biologists BIF-50806)


This course offers an introduction to deep learning algorithms, with emphasis on their applications in life, earth and social science on image, sequence and numeric data. First, the mathematical basis of neural networks will be presented, and then a series of deep learning models will be discussed to solve problems of image segmentation, object detection, representation learning and sequence-based prediction. Every block of the course will include both a theoretical and an applied part, where students will implement their own solutions to solve a specific problem. Implementations will be done in Python. At the end of the course, an applied project in the fields of interest of life, earth or social sciences will be performed by the student as a final assignment. The course is a part of a sequence of courses offered by WUR for students who want to master their knowledge and skills on the relevance of data science for life sciences.  

Learning outcomes:

After successful completion of this course students are expected to be able to:
- explain basic concepts and mathematics underlying neural networks;
- understand and discuss scientific papers in the field of deep learning;
- describe characteristics and typical applications of different neural networks architectures and deep learning algorithms;
- propose deep learning approaches to solve problems in life, earth and social sciences;
- implement and evaluate effective deep learning solutions based on chosen algorithms to solve practical problems.


- lectures;
- tutorials with hands-in coding exercises;
- practicals as computer labs;
- self study (including paper reading).


The examination will consist of:
- a group project during weeks 5 and 6 (50% of the mark);
- a written, close book examination during the final week (50% of the mark).
Each mark will need to be higher than 5.5 to pass the course.


Aston Zhang, Zachary C. Lipton, Mu Li, Alex J. Smola (2020). Dive into deep learning.

Restricted Optional for: MEEEarth and EnvironmentMSc5MO
MBEBiosystems EngineeringMSc5MO
MBEBiosystems EngineeringMSc5MO
MGIGeo-Information ScienceMSc5MO