Fundamentals of Genetics
the Genetic and Molecular Biological Approach to Biology; single-gene inheritance; independent assortment of genes; mapping eukaryote chromosomes by recombination; gene interaction; transcription, processing and translation of RNA in Eukaryotes; regulation of eukaryotic gene expression; genomes and genomics; large-scale chromosomal changes; gene isolation and manipulation; population genetics; inheritance of complex traits.
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
Perspectives on Information and Society
The main aim of the course is to critically reflect on concepts central to Information Studies in terms of assumptions, limitations, and social and ethical implications. As information is the central notion of Information Studies we start with information. We approach information by using hermeneutics; this perspective highlights the characteristics of human understanding and is
Philosophy of A.I. (UU)
This course will make students familiar with fundamental issues in the philosophy of AI, and will introduce them to several current discussions in the field. Students will practice their argumentation and presentation skills, both in class discussions and in writing. The course is split up in three parts. The first part is a quick overview
Population and Quantitative Genetics
Life on earth shows immense variation, both in phenotypes and the underlying genotypes. Population and quantitative geneticists address questions such as where this variation comes from, how it is maintained, and how it can be used. This course introduces seminal models and concepts that deal with the dynamics of genetic variation, and applies these to
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
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
Soil-Plant Relations
Plant roots absorb water and nutrients from the root medium. The bio-availability of these growth factors is determined by a variety of biological, physical and chemical processes and properties. The plant itself affects these processes and soil properties in its rhizosphere. The interactions between the plant and its root environment represent the core of theory