Vorlesung: Advanced Biomedical Systems Modeling and Data Science - Details

Allgemeine Informationen

Veranstaltungsname	Vorlesung: Advanced Biomedical Systems Modeling and Data Science
Veranstaltungsnummer	INF-3815
Semester	WS 2025/26
Aktuelle Anzahl der Teilnehmenden	13
Heimat-Einrichtung	Lehrstuhl für Modellierung und Simulation biologischer Prozesse - Prof. Dr. Andreas Raue
beteiligte Einrichtungen	Fakultät für Angewandte Informatik, Institut für Physik, Medizinische Fakultät
Veranstaltungstyp	Vorlesung in der Kategorie Lehre
Nächster Termin	Donnerstag, 08.01.2026 12:15 - 13:45, Ort: (1054 N)
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile	Ja
Hauptunterrichtssprache	englisch

Lehrende

• Prof.Dr. Andreas Raue

Tutor/-innen

• Cong Quan Ta
• Jiedan Xiao
• Nahom Aregai

Räume und Zeiten

(1054 N)

Donnerstag: 12:15 - 13:45, wöchentlich (14x)

Studienbereiche

• Medizinische Fakultät > Medizininformatik
• Fakultät für Angewandte Informatik > Informatik
• Fakultät für Angewandte Informatik
• Mathematisch-Naturwissenschaftlich-Technische Fakultät > Institut für Physik

Modulzuordnungen

• Medizinische Informatik M.Sc. Medizinische Informatik, PO 2021, 9. Version gültig ab WS 2025/26 > Wahlbereich > INF-3815 - Advanced Biomedical Systems Modeling and Data Science > Advanced Biomedical Systems Modeling and Data Science (Lecture)
• Data Science Master's Programme Data Science, 1. Version gültig ab WS 2025/26 > B. Advanced Data Science Methods > INF-3815 - Advanced Biomedical Systems Modeling and Data Science > Advanced Biomedical Systems Modeling and Data Science (Lecture)
• Austauschstudium - FAI Austauschstudium FAI, 7. Version gültig ab WS 2025/26 > Fakultät für Angewandte Informatik - Master Level > INF-3815 - Advanced Biomedical Systems Modeling and Data Science > Advanced Biomedical Systems Modeling and Data Science (Lecture)
• Austauschstudium - FAI Austauschstudium FAI, 6. Version gültig ab SS 2025 > Fakultät für Angewandte Informatik - Master Level > INF-3815 - Advanced Biomedical Systems Modeling and Data Science > Advanced Biomedical Systems Modeling and Data Science (Lecture)
• Medizinische Informatik M.Sc. Medizinische Informatik, PO 2021, 8. Version gültig ab SS 2025 > Wahlbereich > INF-3815 - Advanced Biomedical Systems Modeling and Data Science > Advanced Biomedical Systems Modeling and Data Science (Lecture)
• Medizinische Informatik M.Sc. Medizinische Informatik, PO 2021, 9. Version gültig ab WS 2025/26 > Medizinische Informatik > INF-3815 - Advanced Biomedical Systems Modeling and Data Science > Advanced Biomedical Systems Modeling and Data Science (Lecture)
• Medizinische Informatik M.Sc. Medizinische Informatik, PO 2021, 8. Version gültig ab SS 2025 > Medizinische Informatik > INF-3815 - Advanced Biomedical Systems Modeling and Data Science > Advanced Biomedical Systems Modeling and Data Science (Lecture)
• Mathematics and Computer Science Master's Programme Mathematics and Computer Science PO 2024, 1. Version gültig ab WS 2025/26 > B. Computer Science > INF-3815 - Advanced Biomedical Systems Modeling and Data Science > Advanced Biomedical Systems Modeling and Data Science (Lecture)

Kommentar/Beschreibung

This interdisciplinary course provides advanced-level knowledge of computational techniques in systems modeling and data science applied to complex biomedical challenges. Students will expand their expertise in mathematical modeling, machine learning, and biological data analysis. The course emphasizes both theoretical understanding and practical application through project-based learning. A solid background in biology, mathematics (calculus, linear algebra, differential equations), and programming (Python) is required. The goal of this course is to prepare students for a career path as scientists in biomedical, biotechnology or pharmaceutical industry, or for continuing their academic training and research by acquiring a PhD degree.

Objectives:
• Deepen understanding of systems modeling and data science principles.
• Develop expertise in handling complex datasets and applying advanced models and methods.
• Enhance proficiency in interpreting and presenting biomedical data using advanced models.
• Strengthen project design and critical evaluation skills for scientific research.

Contents:
Part 1: Data Science
• Omics Data Integration
• Statistical Theory & Applications
• Machine Learning Techniques
• Artificial Ground Truth
Part 2: Systems Modeling
• Dynamical Systems & Modeling Concepts
• Model Development & Analysis
• Experimental Design
• Advanced Model Classes