Systems Analysis and Modelling
This course introduces students to systems approaches for analysing ecological and agro-ecosystems through quantitative simulation models. Students learn to formulate and specify dynamic models, evaluate model performance using statistical methods and inverse modelling, and simulate spatial processes using partial differential equations. Topics include population and nutrient dynamics, dispersal, vegetation patterning, and model calibration. Programming is
Statistical Learning
Statistical learning provides a probabilistic and statistical understanding of topics in machine learning. The overarching goal of the course is to develop methods for estimating (or `learning’) an unknown function from data or making predictions for unseen function outputs. The course aims to empower the student to make a justified decision in adopting machine learning
Gene Technology
Recombinant DNA technology has brought about a revolution in our understanding of molecular processes in living organisms. To date, there is no field in experimental biology that has remained untouched by the potential of isolating, analyzing and manipulating genes and organisms. Thus, gene technology provides essential tools in both fundamental and applied medical, industrial, agricultural,
Algorithms for Network-based Bioinformatics
We will cover topics such as complex network models to characterise and analyse biological systems, approaches to infer network structures from given biological measurements, strategies of network enhancement through network integration, predictions based on network structures, and graph generation (molecular design). Specifically, the course contains the following topics: Background on molecular data: systems biology, data-driven
Evolutionary Algorithms
In this course we consider a specific subfield of Artificial Intelligence: Evolutionary Algorithms (EAs). These algorithms, sometimes also identified as being part of the class of bio-inspired algorithms, have as a metaphor the concept of natural evolution, i.e., the mechanisms by which, the fittest individuals in a population survive, reproduce, and in doing so, over
Data Science for Plant Breeding and Genetics
One of the major goals for plant breeders is to identify candidate varieties that are well adapted to the set of environmental conditions that are relevant for the agricultural system of interest. To achieve this goal, breeders characterize their genotypes in multi-environment trials or in phenotyping platforms. The ranking of genotypes might change across trials,
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
Molecular Systems Biology
Nowadays increasing numbers of complete genomic sequences are available and methods have been developed to study system wide gene expression, protein abundances and interactions and metabolite formation. Systems biology integrates the results of the different omics techniques in order to understand how they work together by using dedicated analysis and visualisation techniques (e.g. machine learning,
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
Introduction to Bioinformatics for Life Sciences
Description: “Introduction to Bioinformatics for Molecular Biologists” is a joint course for the various life science Masters programs at the Utrecht University. This introductory course provides an overview of the importance of bioinformatics in various biological disciplines. While a biological background is required, no programming skills are needed. The course can be considered a general