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
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
Life Science Technology and Society
Maatschappelijke context en implicaties van biotechnologische ontwikkelingen (waaronder biofarmaceutische technologie)
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
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
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
Bioinformatics, Data Analysis
De ontwikkeling van steeds nieuwe en betere high-throughput assay technologieën transformeert de moleculaire biologie in een razendsnel tempo. Sequencing van volledige genomen en de mogelijkheid om RNA, eiwitten, metabolieten en hun interacties op grote schaal (genoom-breed) te meten brengen een revolutie teweeg in biologisch onderzoek. Voor het eerst komen verschillende soorten data kwantitatief beschikbaar, en
Bioinformatics, Data Analysis
De ontwikkeling van steeds nieuwe en betere high-throughput assay technologieën transformeert de moleculaire biologie in een razendsnel tempo. Sequencing van volledige genomen en de mogelijkheid om RNA, eiwitten, metabolieten en hun interacties op grote schaal (genoom-breed) te meten brengen een revolutie teweeg in biologisch onderzoek. Voor het eerst komen verschillende soorten data kwantitatief beschikbaar, en
Artificial Intelligence in (Bio-)Chemical Engineering
The digital transition of the (bio)-chemical industry and research requires new intelligent knowledge and decision-making tools. The increasing availability of data and computational resources over the past decade has led to a resurgence of machine learning-based research. Artificial intelligence has significant advantages over traditional modeling techniques, including flexibility, accuracy, and speed of execution. Therefore, artificial