Forschungseinheitenprofil

TU-Wien > Institut für Information Systems Engineering >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
Associate Prof. Dr.rer.nat.	Andergassen	Sabine
Assistant Prof. Dr.techn.	Bellec	Guillaume
Dipl.-Ing. BSc	Blohm	Peter
	Chen	Florian
Univ.Ass. MSc	De Santis	Francesco Flaviano
Univ.Ass.in Dipl.-Ing.in	Drucks	Tamara
BSc	Furzykov	Roman
Associate Prof. Dr.techn. Dipl.-Ing.	Heitzinger	Clemens
Univ.Ass. MSc	Indri	Patrick
Univ.Ass. Dipl.-Ing. BSc	Jogl	Fabian
	Kacmaz	Ülkühan
Projektass.(FWF) MSc	Khodadadian	Ehsan
Projektass.(FWF) Dr.rer.nat. MSc	Khodadadian	Amirreza
Projektass.(FWF) MSc	Krämer	Marcel Nico
BSc	Kunerth	Philip
Projektass. MSc	Lüderssen	Sebastian Johannes
Univ.Ass. MSc PhD	Malhotra	Sagar
Assistant Prof. Dr.techn. BSc MSc	Neumann	Stefan
Projektass.in(FWF) Dr.in rer.nat. BSc MSc	Parvizi	Maryam
Univ.Ass.in Dott.ssa mag.	Patricolo	Miriam
Projektass.(FWF) Dipl.-Ing. B.A.	Penz	David
Projektass. MSc	Petersen	Johannes Borg Sandberg
Univ.Ass. MSc	Sandrock	Christoph
	Seeliger	Maximilian
BSc	Stonek	Anna

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