Institutsseminar/2021-10-11 Zusatztermin: Unterschied zwischen den Versionen

Aus SDQ-Institutsseminar
(Die Seite wurde neu angelegt: „{{Termin |datum=2021-10-11T14:00:00.000Z |raum=https://conf.dfn.de/webapp/conference/979148706 }} Zusatztermin am Montag um 14 Uhr.“)
 
Keine Bearbeitungszusammenfassung
 
Zeile 1: Zeile 1:
{{Termin
{{Termin
|datum=2021-10-11T14:00:00.000Z
|datum=2021-10-11T14:00:00.000Z
|raum=https://conf.dfn.de/webapp/conference/979148706
|online=https://conf.dfn.de/webapp/conference/979148706
}}
}}
Zusatztermin am Montag um 14 Uhr.
Zusatztermin am Montag um 14 Uhr.

Aktuelle Version vom 14. Januar 2022, 13:14 Uhr

Termin (Alle Termine)
Datum Montag, 11. Oktober 2021
Uhrzeit 14:00 – 14:45 Uhr (Dauer: 45 min)
Ort
Webkonferenz https://conf.dfn.de/webapp/conference/979148706
Vorheriger Termin Fr 24. September 2021
Nächster Termin Fr 15. Oktober 2021

Termin in Kalender importieren: iCal (Download)

Vorträge

Vortragende(r) Lena Witterauf
Titel DomainML: A modular framework for domain knowledge-guided machine learning
Vortragstyp Masterarbeit
Betreuer(in) Pawel Bielski
Vortragssprache
Vortragsmodus
Kurzfassung Standard, data-driven machine learning approaches learn relevant patterns solely from data. In some fields however, learning only from data is not sufficient. A prominent example for this is healthcare, where the problem of data insufficiency for rare diseases is tackled by integrating high-quality domain knowledge into the machine learning process.

Despite the existing work in the healthcare context, making general observations about the impact of domain knowledge is difficult, as different publications use different knowledge types, prediction tasks and model architectures. It further remains unclear if the findings in healthcare are transferable to other use-cases, as well as how much intellectual effort this requires.

With this Thesis we introduce DomainML, a modular framework to evaluate the impact of domain knowledge on different data science tasks. We demonstrate the transferability and flexibility of DomainML by applying the concepts from healthcare to a cloud system monitoring. We then observe how domain knowledge impacts the model’s prediction performance across both domains, and suggest how DomainML could further be used to refine both the given domain knowledge as well as the quality of the underlying dataset.

Neuen Vortrag erstellen

Hinweise

Zusatztermin am Montag um 14 Uhr.