| Dozent: | Prof. Dr. Hans A. Kestler |
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Dr. Ludwig Lausser
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| Code: | CS8006.000 |
Vorlesungszeiten und Ankündigung
Inhalt und Themen
Die Veranstaltung vermittelt Grundkenntnisse Evolutionärer Algorithmen. Es werden die derzeit gängigen Verfahren vorgestellt. Lernziele sind dabei die eigene Implementation der Algorithmen und deren Bewertung im Hinblick auf Anwendung und sinnvollen Einsatz. Weiterhin sollen Grundlagen und praktisch verwendbare Kenntnisse und Fähigkeiten vermittelt werden, die es erlauben eigene Problem-repräsentationen zu erstellen und entsprechende EA Bausteine zu entwickeln bzw. zu erweitern.
Folien
Evolutionäre Strategien/Programmierung
Folien
Sonstiges
Congratulations to Dr. Silke Werle for winning the 1st Prize with her pitch at the 1. Science Day held by ProTrainU.
Our paper "Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells" has been published in the Computational and Structural Biotechnology Journal.
The position paper "Is there a role for statistics in artificial intelligence" has been published online first in Advances in Data Analysis and Classification.
Our paper "Corona Health - A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic" has been published in the International Journal of Environmental Research and Public Health.
Our paper "Patient empowerment during the COVID-19 pandemic: Ensuring safe and fast communication of test results" has been published in the Journal of Medical Internet Research.
Our paper "Perspective on mHealth Concepts to Ensure Users’ Empowerment–From Adverse Event Tracking for COVID-19 Vaccinations to Oncological Treatment" has been published in IEEE Access.
Our report protocol "Digitalization of adverse event management in oncology to improve treatment outcome—A prospective study protocol" has been published in PLoS One.
We are happy we could contribute to Beutel et al (2021) "A prospective Feasibility Trial to Challenge Patient-Derived Pancreatic Cancer Organoids in Predicting Treatment Response" published in MDPI Cancers.