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| Describe Main/SS17_ML2 here. | = Maschinelles Lernen - Theorie und Anwendung = === General Information === Maschinelles Lernen - Theorie und Anwendung is a 9 LP (9 ECTS) credits module. ||'''Lecture period'''|| from 20.04.2017 to 20.07.2017 || ||'''Lecture'''|| Thursday 10:15-12:00 in MAR 0.011 || ||'''Exercise session'''|| Thursday 12:15-14:00 in MAR 0.011 || ||<(^|2>'''Trainers'''||Prof. Dr. Klaus-Robert Müller (Responsible)|| ||Gregoire Montavon || ||'''Contact''' || gregoire.montavon@tu-berlin.de || || '''ISIS''' || TBD || === Topics === * Embeddings * Stationary Subspace Analysis * Independent Component Analysis * Canonical Correlation Analysis * Kernel methods for structured data * Neural networks for structured data * Unsupervised neural networks * 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 period |
from 20.04.2017 to 20.07.2017 |
Lecture |
Thursday 10:15-12:00 in MAR 0.011 |
Exercise session |
Thursday 12:15-14:00 in MAR 0.011 |
Trainers |
Prof. Dr. Klaus-Robert Müller (Responsible) |
Gregoire Montavon |
|
Contact |
|
ISIS |
TBD |
Topics
- Embeddings
- Stationary Subspace Analysis
- Independent Component Analysis
- Canonical Correlation Analysis
- Kernel methods for structured data
- Neural networks for structured data
- Unsupervised neural networks
- Structured output learning
- One-class SVMs
- Bioinformatics