This course provides an overview of central statistical and algorithmic concepts in the field of machine learning in the area of bioinformatics.

Topics will include:

- Regularization methods for variable selections and regression methods for features decorrelation with application to Mass Spectroscopy data and Cancer data
- Support Vector Machines for tumor classification based on genomic data and clinical covariates
- SVMs with string kernels to classify omics data, such as RNA sequences
- Artificial Neural Networks (ANNs) and Deep Learning and some recent applications in bioinformatics
- Graphical models for signal cascade analysis and quasi-species identification
- Active learning with Random Forests applied to modern omics data, such as Mass Specrometry or NGS
- Unsupervised learning: model-based clustering of microRNA expression data

After successful completion of this course, participants are able to classify and apply the learned techniques in the context of current research in bioinformatics, biology and biotechnology. They are able to select suitable methods and models for specific problems and are able to evaluate and communicate the results.