Fachbereich Informatik

Teaching

Current Semester

WINTER 2020/2021

Unless stated otherwise, our lectures will be online and tutorials and seminars will take place in presence (exceptions possible).

  • Lecture: Advanced Medical Informatics (9 ECTS)
    Please visit ILIAS course page for course material and registration.
    This lecture comprises different areas of Medical Informatics. The focus is on data integration, medical data privacy, artificial intelligence and data mining for health data, and treatment decision support systems. Specific topics include:
    • statistical machine learning basics
    • state-of-the-art in decision support systems and beyond
    • differential privacy
    • k-anonimity
    • privacy-preserving record linkage
    • federated learning approaches and GO-FAIR
    • genome privacy
    • FHIR
    • openEHR
    • data warehouses and no-SQL data bases
    • MapReduce
       
  • Seminar: Machine Learning for Health
    In this seminar, we will discuss recently published research articles about machine learning on biomedical data in a broad sense. For further information and pre-registration, please visit moodle.
     
  • Lecture: Medical Data Science (2+2 SWS => 6 ECTS)
    Please visit ILIAS course page for course material and registration.
    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 include:
    • 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)
       
  • Newly enrolled students in the Master's degree program in Medical Informatics with a background in Medicine/Pharmacy/Life Sciences, please also see the Preparatory Course in Programming for Life Scientists site.

Master's degree program in Medical Informatics

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.

Please check our group members’ research interests to find the most suitable contact person for your preferred field.

We are looking forward to discussing further details with you!

Previous Teaching

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