Control Engineering
Besides a correct design or layout, good control systems are essential to guarantee that production systems operate and produce according the desired specifications. This course gives an introduction to classical control engineering approaches and discusses the standard methods and tools that are usually applied. The methods discussed in the course have a very wide application
Exploratory Data Analysis in R
lant scientists use a wide range of experimental tools. Be it a simple weight measurement or an advanced imaging analysis, every experiment generates data that must be analyzed. Modern approaches for reproducible data analysis are mostly based on writing scripts in (statistical) programming languages. As such, working in the plant sciences and broader life sciences
Genome Bioinformatics
Complex biological systems arise from interactions between molecules, cells, organisms, and their environment. The genome serves as the blueprint for these interactions and is shaped by 4 billion years of evolution. With advances in sequencing technologies, researchers can now collect large-scale genomic datasets from species, populations, and environments. In this course, students explore how such
Machine Learning 1
This course is lecture based, with homework assignments and programming assignments. The curriculum is based on chapters 1,2,3,4,5,6,7,9,14 of the book Pattern Recognition and Machine Learning by C. Bishop: Statistical learning principles; Linear regression; Linear classification; Neural networks; Kernel methods; Dimensionality reduction; Clustering methods; Ensemble methods
Digital innovation
In the course Digital Innovation we focus on the role of Information and Communication Technology (ICT) in the fundamental transformations of industries and societies. After introducing the nature of ICTs, the first part of the course starts by providing an historical and theoretical introduction to digital innovation from a societal perspective. Specific emphasis is on
Research in Bioinformatics
Bioinformatics is at the heart of many modern systems biology analyses, and encompasses the application of statistics and computer science to (large-scale) biomolecular datasets. In essence, bioinformatics is about smart ways of extracting knowledge from the enormous amounts of data that can be generated using modern measurement techniques. For instance, it plays an important role
Molecular and Cellular Biophysics
De combinatie van de vakken Moleculaire Celbiologie, Life Sciences en Biochemie geven een complete introductie van de werking van de levensprocessen in de cel. Studenten maken kennis met de moleculaire wereld en leren een moleculaire manier van denken aan. In de Moleculaire Celbiologie nemen we het functioneren van de kleinste levende eenheid, de cel, als
Keystone Project III: Big Data
Welke genen worden geactiveerd bij stress? Waarom bevorderen sommige bacterieën immuniteit? Hoe ontstaat kanker? Hoe wordt een microbe een ziekteverwekker? Om dit soort vragen te beantwoorden worden er vaak grote datasets –“Big Data” gecreëerd. In deze gevallen is analyse van de resultaten met eenvoudige middelen (Excel etc.) niet meer werkbaar. Om hiermee om te gaan
Plant Biotechnology (online)
Plant biotechnology is a discipline that connects the new insights into genes and their products with the end product that is destined for the market. As with other technological disciplines, plant biotechnology comprises a mixture of many other scientific areas of biological sciences such as molecular biology, biochemistry, physiology and genetics. In this course we
Machine Learning (WUR)
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