Modeling and Data Analysis in Complex Networks
Big Data is mostly obtained from features of components and the interactions between components in large complex systems. Examples are (1) end user features and interactions in both online and real-world social networks like Twitter, LinkedIn (2) data from content sharing platforms such as YouTube (3) physiological data of the brain and (4) stock prices
Practical Computing for Life Scientists
This course offers a practical introduction to advanced computer use for the analysis of biological data. It is focused on technical aspects of handling large data files, working on remote computers running the Linux operating system using the command line (shell), and developing practical programming/scripting skills. There will be some emphasis on biological molecules (DNA,
Introduction to Machine Learning 1
The core of this course revolves around programming machine learning algorithms for yourself, as a way to truly understand what exactly they are learning. Each of the different modules will focus on programming a different algorithm, understanding the math required for that algorithm, and discussing a philosophical question or a societal impact related to applying
Basic Machine Learning for Bioinformatics
Modern biology increasingly relies on vast datasets generated through genomic, transcriptomic, and single-cell omics techniques. To make sense of these large-scale data and extract meaningful patterns or predictions, machine learning (ML) has become essential. This course introduces students to the foundational principles and techniques of ML as applied to biological data. Students learn the differences
Plant Breeding
This course introduces students to key principles of plant breeding. Plant breeding is the science-driven creative process of changing the traits of plants in order to develop new plant varieties. Several essential approaches and tools used in the process are discussed: different modes of reproduction, selection methods, including molecular selection methods, the production of hybrid
Introduction to Machine Learning (TU Delft)
This course equips students with foundational understanding of key concepts of Machine Learning (ML) and demonstrates how to solve real world problems with ML techinques. It covers the following topis: Learning Theory, Supervised Learning, Unsupervised Learning, and Transfer and Ensemble Learning
Biotic Interactions
This course provides knowledge on recent developments in research on plant-pathogen and plant-insect interactions. This will include the molecular targets and signal transduction pathways involved, and the ecological aspects of biotic interactions in nature and in agriculture. With regard to defense against pathogens, the innate immune response and the gene-for-gene model will be discussed in
Data Management
This course covers database design and the use of databases in applications, with a focus on applications in the life sciences. Topics include the relational model, database design principles, the structured query language (SQL), including temporal and spatial queries. Data lifecycle topics and contemporary issues for data scientists and practitioners are also introduced, i.e. big
Moleculaire Celbiologie & Immunologie
Tijdens dit vak zullen de basisprincipes van moleculaire regulaties in menselijke, of dierlijke cellen behandeld worden, met een speciale focus op cellulaire functies/interacties tijdens immuunreacties. De basisprincipes van de regulatie van celgroei, cel specificatie en de communicatie in en tussen cellen wordt behandeld. Er wordt een basisintroductie van het immuunsysteem gegeven en er zullen een
Plant Physiology and Development
Het centrale thema in deze cursus is het functioneren van de plant in relatie tot haar omgeving. Omdat planten niet mobiel zijn, stemmen zij hun groei en ontwikkeling voortdurend af op veranderende omgevingsfactoren, zoals (a)biotische stress en seizoensgebonden signalen. De cursus behandelt zowel basale processen die nodig zijn voor groei en ontwikkeling, als de moleculaire