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Embedding
Stationary Subspace Analysis
Auto-encoders
Canonical Correlation Analysis
Kernel methods for structured data
Neural networks for structured data
Structured output learning
One-class SVMs
Bioinformatics		 * Embedding
* Stationary Subspace Analysis
* Auto-encoders
* Canonical Correlation Analysis
* Kernel methods for structured data
* Neural networks for structured data
* Structured output learning
* One-class SVMs
* Bioinformatics

Maschinelles Lernen - Theorie und Anwendung

General Information

Maschinelles Lernen - Theorie und Anwendung is a 9 LP (9 ECTS) credits module.

Lecture

Tuesdays, 10 - 12

Room

MAR 0.015

Exercise session

Tuesdays, 12 - 14

Room

MAR 0.015

Trainers

Prof. Dr. Klaus-Robert Müller (Responsible)

Gregoire Montavon

Contact

gregoire.montavon@tu-berlin.de

ISIS

https://isis.tu-berlin.de/course/view.php?id=4266

Topics

* Embedding * Stationary Subspace Analysis * Auto-encoders * Canonical Correlation Analysis * Kernel methods for structured data * Neural networks for structured data * Structured output learning * One-class SVMs * Bioinformatics

IDA Wiki: Main/SS15_MaschinellesLernen2 (last edited 2015-06-29 08:48:33 by GrégoireMontavon)