Forschungseinheitenprofil

TU-Wien > Fakultät für Informatik >Institut für Information Systems Engineering >Forschungsbereich Machine Learning

Leitbild (Deutsch) Our research aims to narrow the gap between theoretically well-understood and practically relevant machine learning. Research questions concern for instance:

learning with non-conventional data, i.e., data that has no inherent representation in a table or Euclidean space
incorporation of invariances as well as expert domain knowledge in learning algorithms
computational, sample, query, and communication complexity of learning algorithms
constructive machine learning scenarios such as structured output prediction
learning with small labelled data sets and large unlabelled data sets
adverserial learning with mistake and/or regret bounds
parallelisation/distribution of learning algorithms
approximation of learning algorithms
scalability of learning algorithms
reliability of learning algorithms
extreme learning
...

To demonstrate the practical effectiveness of novel learning algorithms, we apply them in Chemistry, Material Science, Electrical Engineering, Computer Games, Humanities, etc.

Leitbild (Englisch) Our research aims to narrow the gap between theoretically well-understood and practically relevant machine learning. Research questions concern for instance:

learning with non-conventional data, i.e., data that has no inherent representation in a table or Euclidean space
incorporation of invariances as well as expert domain knowledge in learning algorithms
computational, sample, query, and communication complexity of learning algorithms
constructive machine learning scenarios such as structured output prediction
learning with small labelled data sets and large unlabelled data sets
adverserial learning with mistake and/or regret bounds
parallelisation/distribution of learning algorithms
approximation of learning algorithms
scalability of learning algorithms
reliability of learning algorithms
extreme learning
...

To demonstrate the practical effectiveness of novel learning algorithms, we apply them in Chemistry, Material Science, Electrical Engineering, Computer Games, Humanities, etc.

Webseite https://www.ml.tuwien.ac.at/

Personen

Titel	Nachname	Vorname
Assistant Prof. Dr.in rer.nat.	Andergassen	Sabine
Univ.Ass.in Dipl.-Ing.	Drucks	Tamara
Univ.Ass. MSc	Indri	Patrick
Projektass.in	Jahanjoo	Anice
Univ.Ass. Dipl.-Ing. BSc	Jogl	Fabian
	Kacmaz	Ülkühan
Univ.Ass. Dipl.-Ing. B.A.	Penz	David
Univ.Ass. MSc	Thiessen	Maximilian
	Trauner	Martina

Fachgebiete nach Statistik Austria

Code	Fachgebiet(Deutsch)	Fachgebiet(Englisch)
1122	Artificial Intelligence	Artificial intelligence

Schlagwörter

Schlagwort (Deutsch)	Schlagwort (Englisch)
Maschinelles Lernen	Machine Learning
Data Mining	Data Mining