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| 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 === [[https://www.isis.tu-berlin.de/course/view.php?id=7690|ISIS Website]] |
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| 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 work in practice, are simple to implement and scalable to big data. |
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| 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 |
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Verantwortlicher: |
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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)