Fachbereich Informatik

Teaching

Current Semester

WINTER 2021/2022

Please check if your course takes place online or in presence, there might be changes during the semester.

  • Lecture: Advanced Medical Informatics
    This lecture comprises different areas of Medical Informatics. The focus will be on data integration, medical data privacy, artificial intelligence and data mining for health data, and treatment decision support systems.
    Go to ILIAS course page: link
     
  • Lecture: Medical Data Science
    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. 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)
      Go to ILIAS course page: link
       
  • Seminar: Machine Learning for Health
    This seminar will cover different machine learning methods on biomedical data to answer medical questions of interest. For pre-registration and further information visit the ILIAS course page: link

See also: IBMI Programming Helpdesk with focus on Java and Python.

Master's degree program in Medical Informatics

First semester students in the Medical Informatics master's program - winter term 2021/2022: please see Prof. Pfeifer's Welcome message and useful information.

____________________________

Thank you for your interest in studying Medical Informatics at the University of Tübingen.

Our Master's degree program in Medical Informatics will be a perfect fit for you if you have a strong affinity for Computer Science and programming skills in general.

Your eligibility will depend on your previous qualifications in Computer Science and/or Medicine/Pharmacy. Knowledge in Biostatistics and Python would be particularly useful.

Language requirements:

  • Our Master's degree program in Medical Informatics is an English language program, therefore Level B2 skills in English are required.
  • A Level B2 certificate in German would only be required in case of attending certain eligible modules that are offered in German only.

Further details:

For application deadlines, visit Application and Enrollment.

Online application for our Master’s programs: www.alma.uni-tuebingen.de/alma/pages/cs/sys/portal/hisinoneStartPage.faces.


Should you require any further information, please contact our Student Affairs Department: studyspam prevention@uni-tuebingen.de.

We are looking forward to welcoming you soon as our student.

Thesis Topics

We are offering a range of topics for Master’s and Bachelor’s theses and research projects.

Feel free to check our group members’ research interests to find the most suitable contact person for your preferred field, or contact us to explore any specific areas for supervision.

You can download our Medical Informatics (or Bioinformatics) Master's Thesis Template here.

We are looking forward to discussing further details with you!

Previous Teaching

SUMMER 2021

  • Lecture: Introduction to Statistical Machine Learning for Bioinformaticians and Medical Informaticians
    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. Programming tasks require submission in R.
  • Seminar: Machine Learning for Health
    In this seminar, we discuss recently published research articles about machine learning on biomedical data.
  • Newly enrolled students in the Master's degree program in Medical Informatics with a background in Medicine/Pharmacy/Life Sciences can attend a Preparatory Course in Programming for Life Scientists.

WINTER 2020/2021

SUMMER 2020

WINTER 2019/2020

  • Lecture: Advanced Medical Informatics
  • Practical Course: Machine Learning in Biomedicine
  • Seminar: Machine Learning for Health
  • Seminar: Biomedical Informatics Methods for Infection Research
     

SUMMER 2019

  • Lecture: Medical Data Science
  • Lecture: Bioinformatics for Life Scientists
  • Seminar: Computer Science Methods for Privacy Preservation and Personalized Medicine
  • Seminar: Probabilistic Graphical Models and Probabilistic Programming
  • Software Project in Medical Informatics
     

WINTER 2018/2019

  • Lecture: Advanced Medical Informatics
  • Practical course: Machine Learning in Biomedicine
  • Seminar: Machine Learning for Health
     

SUMMER 2018

  • Lecture: Medical Data Science
  • Lecture: Bioinformatics for Life Scientists
  • Seminar: Computer Science Methods for Privacy Preservation in Biomedical Studies
  • Software Project in Medical Informatics
     

WINTER 2017/2018

  • Lecture: Advanced Medical Informatics
  • Practical course: Machine Learning in Biomedicine
  • Seminar: Machine Learning for Health
     

SUMMER 2017

  • Lecture: Medical Data Science
  • Seminar: Biomedical Informatics Methods for Infection Research
  • Software Project in Medical Informatics