|
Size: 1757
Comment:
|
Size: 1759
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 1: | Line 1: |
| == Kognitive Algorithmen == | === Kognitive Algorithmen === Computer programs can learn useful cognitive skills. This lecture tries to communicate an intuitive understanding of elementary concepts in machine learning, their historical development and their application on real data with a special focus on methods that are simple to implement. Computerprogramme können nützliche kognitive Fähigkeiten lernen. Diese Veranstaltung vermittelt ein intuitives Verständnis elementarer Konzepte des Maschinellen Lernens, deren Entstehung und ihrer Anwendung. |
| Line 6: | Line 12: |
| === Termine und Dozenten === | == Termine und Dozenten == |
| Line 13: | Line 19: |
| === Topics === | == Topics == |
| Line 15: | Line 21: |
| Computer programs can learn useful cognitive skills. This lecture tries to communicate an intuitive understanding of elementary concepts in machine learning, their historical development and their application on real data with a special focus on methods that are simple to implement. |
|
| Line 28: | Line 32: |
| Computerprogramme können nützliche kognitive Fähigkeiten lernen. Diese Veranstaltung vermittelt ein intuitives Verständnis elementarer Konzepte des Maschinellen Lernens, deren Entstehung und ihrer Anwendung. |
Kognitive Algorithmen
Computer programs can learn useful cognitive skills. This lecture tries to communicate an intuitive understanding of elementary concepts in machine learning, their historical development and their application on real data with a special focus on methods that are simple to implement.
Computerprogramme können nützliche kognitive Fähigkeiten lernen. Diese Veranstaltung vermittelt ein intuitives Verständnis elementarer Konzepte des Maschinellen Lernens, deren Entstehung und ihrer Anwendung.
Anmeldung / Registration
Termine und Dozenten
Termin: |
Vorlesung: Mi. 12:00 - 14:00, 24.10.2012 - 13.02.2013 |
Raum: FR 3002 |
|
Verantwortlicher: |
|
Betreuer: |
Topics
We will cover (among other things)
- Supervised learning (linear regression techniques, kernel based regression)
- Artificial Neural Networks (Reichardt Correlator, Perceptron Algorithm, Multilayer Neural Networks)
- Dimensionality reduction (Principal Component Analysis, Canonical Correlation Analysis)
- Clustering (k-Means)
- Model Selection
Themen
Vorgestellt werden unter anderem:
- Überwachte Lernmethoden (lineare Regression, naive Bayes Klassifikation)
- Künstliche Neuronale Netze (Reichardt Korrelator, Perzeptron Algorithmus, Multilayer Neural Networks)
- Dimensionsreduktion mit Faktoranalysen (Principal Component Analysis, Canonical Correlation Analysis)
- Clustering (k-Means, Spectral Clustering)