Deep Learning 1
Deep learning is primarily a study of multi-layered neural networks, spanning over a great range of model architectures. We cover the basic following content in theory and in practice. We adapt the content, especially in the last lectures, based on the latest advances: Introduction to deep learning. A brief introduction of deep learning. History, recent
Intro to ML, generative AI, programming in python
Life-long learning: Masterclasses (individual meetings or modules/e-learnings) and a number of short courses
Machine Learning in Bioinformatics
Learning from patterns in molecular biology data plays an important role in diagnosing disease, discovering new targets for therapy, and more generally in answering biological questions that lead to an improved understanding of biological systems with relevance to human health, industry, biotechnology, and agriculture. This course focuses on methodology for the analysis of high-dimensional data
Tools in Molecular Data Analysis
This course aims to develop practical skills in data handling, analysis and visualization for large “omics” data commonly found in contemporary plant research. Two domain-specific projects will be addressed: i) RNA-seq analysis and ii) Microbiome analysis. For each project, after introductory lectures on the topic, students will be trained to handle these specific datasets. Afterwards,