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| Describe Main/SS15_MaschinellesLernen2 here. | = 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|| ||<(^|2>'''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 |
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 |
|
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