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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 |
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Contact |
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ISIS |
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