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
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
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
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
Societal Challenges & Innovation Theory
The analysis of innovation processes is much more powerful when using theories of socio-technical change and innovation. Theories of socio-technical change and innovation can be a powerful tool to understand and help solve some of the grand challenges that our society faces. The course focuses on the UN Sustainable Development Goals (SDGs), like SDG 3
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
Trends in Plant Reproduction Biology: from Flowers to Seeds
The evolutionary success and enormous diversity among flowering plants is mainly the result of the sexual reproduction process, which starts with flowering and results in the formation of fruits and seeds. This key process is under tight genetic and molecular control, but is also adapted to the environment. How such a conserved process can show
Abiotic Stress
Environmental (abiotic) stress is the most important limiting factor in crop productivity. This master course aims to broaden the student’s horizon in abiotic-stress biology and to gain the latest insights into the molecular mechanisms by which plants perceive such stress signals, how they are transduced, and how stress signals are eventually converted into intracellular responses