Fachbereich Informatik


  • Lecture: Medical Data Science (2+2 SWS => 6 ECTS)
    Please also visit ILIAS.
    This lecture comprises different areas of Medical Data Science. Data Science or statistical machine learning methods have the potential to transform personal health care over the coming years. Advances in the technologies have generated large biological data sets. In order to gain insights that can then be used to improve preventive care or treatment of patients, these big data have to be stored in a way that enables fast querying of relevant characteristics of the data and consequently building statistical models that represent the dependencies between variables. These models can then be utilized to derive new biomedical principals, provide evidence for or against certain hypotheses, and to assist medical professionals in their decision process. Specific topics are:
    • Gaining new insights from medical data
    • Modeling uncertainty in medical data science models
    • Making medical findings available through interpretable decision support systems
    • Method-wise, the lecture will introduce methods for GWAS analyses (e.g., LMMs), methods for sequence analysis (e.g., kernel methods), methods for “small n problems” (e.g., domain adaptation, transfer learning, and multitask learning), methods for data integration (advanced unsupervised learning methods), methods for learning probabilistic Machine Learning models (e.g., graphical models), methods for large data sets (e.g., deep learning models)
  • Lecture: Introduction to Statistical Machine Learning for Bioinformaticians and Medical Informaticians (2+2 SWS => 6 ECTS)
    This lecture provides an introduction into statistical machine learning with a focus on practical application in (biomedical) data analysis. It comprises basic methods for supervised (classification, regression) and unsupervised learning.
    Topics include but are not limited to:
    • Linear models for regression and classification, model selection & regularization, cross validation, bootstrap, decision trees, random forest, boosting, support vector machines, dimensionality reduction, clustering methods
    • Primary textbook for the lecture is James, Witten, Hastie, Tibshirani: Introduction to Statistical Learning
    • Programming tasks require submission in R.
    • All further course information, including updates regarding organization in the age of COVID-19, can be found in moodle.
  • Lecture: Bioinformatics for Life Scientists (2 SWS => 3 ECTS)
    Further information on ILIAS.
  • Software Project in Medical Informatics
  • Seminar: Computer Science Methods for Privacy Preservation and Personalized Medicine
    This seminar will cover different computer science methods for privacy preservation in biomedical data sharing as well as for personalized medicine. For pre-registration please email Nico Pfeifer.
    Further information on ILIAS.


Previous semester(s)