Module Number

BIOINF4384 (entspricht BIO-4384) 		Module Title

Machine Learning of Single-Cell Dynamics 		Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Workload:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Excercises (not graded) must be passed. Written / oral exam (graded).
Content

Single-cell technologies have been used to reconstruct the dynamics of biological processes, such as signaling, differentiation and development. This course will review different types of technologies that have been developed and used to this end. At the core, this lecture will introduce and discuss different mathematical models for cellular dynamics, as well as classical and machine learning based system identification and model selection approaches to learn such models from single-cell data.
Objectives

(1) Overview of time resolved single-cell technologies
(2) Dynamic models for cellular systems
(3) Systems identification and model selection for dynamic models
(4) Machine learning for systems identification and model selection
Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Claassen
Literature

Requirements: Programming skills in Python.
Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas BIO-BIO, ML-DIV