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
Plant Breeding and Biotechnology
This course provides knowledge on recent developments in plant breeding and future prospects in plant biotechnology. A thorough understanding of genetics, plant breeding and biotechnology tools including genetic modification and gene editing is linked to applications in various fields (crop improvement, biofortification, soil remediation and biofuel production). After a general introduction into a field, recent
Bioinformatics and Evolutionary Genomics
The sequencing revolution is rapidly charting ever more obscure branches in the tree of life. All these novel genomes and increasingly sophisticated bioinformatic methods are changing our view of where the complexity in our protein complexes come from and how the genes in the human genome came from. For example when we trace compare the
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
Data Analysis for Plant and Animal Breeding
Data analysis is central to both plant and animal breeding, and the size and complexity of phenotypic and genomic data sets continue to increase. Thus, the ability to analyze and interpret such large data sets is an essential skill for breeders, both in science and industry. In this course you will become familiar with state-of-the-art
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
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