Machine Learning (TU Delft)
The goal of the course is to acquaint students with the basic Machine Learning concepts and algorithms. Specifically, the course will cover parametric and non-parametric density estimation, linear and non-linear classification, unsupervised learning including clustering and dimensionality reduction, performance evaluation of predictive algorithms and ethical issues in machine learning.
Intro to ML, data science and information systems, but also management, leadership and transitions
Life-long learning: Various short modules offered partly in-person and partly online (multiple meetings)
Data Science and Biology
This course focuses on applying data science methods to biological data. With the rise of high-throughput techniques in biology, massive datasets are now common, including genomes, metagenomes, transcriptomes, and more. The course equips students with the theoretical and practical skills to extract insights from such data. Students learn to work with the command-line interface, write
Modelling biological systems
Biological systems are arguably the most complex systems science tries to understand. We make observations, and try to obtain useful insight in the systems. This requires hypotheses; ideas of how we believe the world around us is structured and operates. Models translate our hypotheses into concrete predictions about our objects of study that can be
Modelling and Problem Solving
The course covers major (combinatorial) solving approaches, namely constraint programming, integer programming, boolean satisfiability, dynamic programming and decision diagrams, and local search. The course focuses on modelling practical problems, expressing them in the surveyed paradigms; algorithms for solving such problems are covered in the follow-up “Constraint Solving” course. The topic of genetic algorithms is reserved
Master Level Computational Biology
During the course, the emphasis will be on composing and analysing exact computational models based on specific biological hypotheses. These models are used to gain insight into the dynamics of biological systems across various fields. Topics include multi-level evolution, such as pre-biotic evolution, eco-evolutionary dynamics, spatial pattern formation, and genome evolution—for instance, the interaction between
Algorithms in Bioinformatics
Modern biology routinely generates huge amounts of data: sequences, from NGS experiments; quantitative data, from -omics experiments; and graphs, representing molecular interactions. At the heart of many bioinformatics applications are algorithms that handle such types of data in time- and memory-efficient ways. Almost invariably these algorithms optimize some criterion – e.g. alignment quality, energy function
Bioinformatics and Dynamic Modelling
This course introduces students to the research fields of bioinformatics and biological modeling. Central themes are the use of data to extract underlying patterns on function and evolution, and the use of models to test hypotheses and make predictions for biological systems. Introduction: Biological processes are notoriously complex, and studying their dynamics through modeling and
Applied Machine Learning
Machine learning is marking a revolution in the world. From an academic research topic, over the last decade it has shift to a major paradigm used in many companies for a wide range of services. From deleting SPAM mail from your inbox to ranking the Google search results, and from defining your Facebook stream to