Master Seminar Machine Learning for Medical Data

Responsible for the Module: Prof. Dr. Paul Swoboda, Lecturer(s): Prof. Dr. Paul Swoboda, Jan Benedikt Ruhland

Language: English, Work Load: 60 h, Credits: 5 CP, Contact Time: 30 h, Self-study: 120 h, Course Seminar: 2 SWS, Turnus: Irregular, Group Size: Limited to 24, Duration: 1 Semester

Content: Discriminative and generative models, Biomarker discovery, Causality, LLMs and Omics, Industrial transfer and other ML applications

Learning results & Competences: After completion students will have an in-depth understanding of various recent and classical advances in the field of machine learning applications for medicine. They can independently review and evaluate scientific publications and give technical presentations on the covered subjects.

Prerequisites for attending: Contentual: Machine Learning

Examination: Assessment of presentation and written report

Prerequisites for receiving credit points: 

• Regular attendance in person
• Presentation of one topic
• Active participation in discussions
• Written report
• Mid-term progress presentation

Study Program: M.Sc. Artificial Intelligence and Data Science

Module accessible for other Study Programs

Weight in overall rating: The mark given will contribute to the final grade in proper relation to its credit points.