inf5454 - Current Topics of Machine Learning in (bio-)medicine (Complete module description)

Knowledge in the basics in the area of machine learning and / or deep learning Previous knowledge is desirable, knowledge in the analysis of (bio)medical data is also advantageous

Professional competence
The students

• gain an exemplary overview of application areas of machine learning in biomedicine and can contextualize the discussed topics within broader methodological and application contexts

Methodological competence
The students

• can independently explore topics using current research literature and critically reflect upon them.

Social competence
The students

• can present solution approaches for problems in this area to the plenary and defend them in discussions.

Self-competence
The students

• are able to assess their own subject-specific and methodological competence. They take responsibility for their competence development and learning progress and reflect on these independently. In addition, they independently work on learning content and can critically reflect on the content.

This seminar provides insights into various application contexts of  machine learning methods, especially deep learning, in the (bio)medical  field. Depending on the instantiation of the module, different emphases  will be placed, such as current examples of machine learning methods for  diagnostic support, analysis of multimodal data, and even the analysis  of protein data.

For background reading, see 

• Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature medicine, 28(1), 31-38. 

Relevant specialized readings will be recommended throughout the course.

at the end of the lecture period/ mid-term exams

oral exam or portfolio or presentation