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Kognitive Algorithmen

Blockseminar

Termine und Dozenten

Termin:	Vorbesprechung 11.04.2012
Termin:	Raum: FR 6046
Verantwortlicher:	Prof. Dr. Klaus-Robert Müller
Betreuer:	Dr. Felix Bießmann, Irene Winkler

Themen

Computerprogramme können nützliche kognitive Fähigkeiten lernen. Dieses Seminar erlaubt eine vertiefende Einarbeitung in spezielle Anwendungsgebiete von Algorithmen aus dem Bereich des Maschinellen Lernens wie etwa automatisierte Informationsextraktion aus Texten, Mustererkennung in hochdimensionalen Daten, explorative Datenanalyse.

Unter Anleitung wird englischsprachige Fachliteratur über ausgewählte Anwendungsbeispiele zu analysieren, kritisch zu evaluieren und verständlich zu präsentieren sein.

Voraussetzungen

Grundlegende Konzepte der Wahrscheinlichkeitsrechnung, Statistik und Linearer Algebra.

Papers

Thema	Betreuer	Student
A simplified neuron model as a principal component analyzer
Spatiotemporal energy models for the perception of motion
Eigenfaces for Recognition
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Gradient-Based Learning Applied to Document Recognition
Matrices, Vector Spaces, and Information Retrieval
Support vector machines for spam categorization
Kernel PCA and De--Noising in Feature Spaces
Unsupervised Learning by Probabilistic Latent Semantic Analysis
Rapid Object Detection using a Boosted Cascade of Simple Features
Kernel methods for exploratory pattern analysis: a demonstration on text data
The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects
Decoding the visual and subjective contents of the human brain

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+|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Oja1982.pdf|A simplified neuron model as a principal component analyzer]] || ||  ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Adelson1985.pdf|Spatiotemporal energy models for the perception of motion]] || ||  ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Turk1991.pdf|Eigenfaces for Recognition]]|| || ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Olshausen1996.pdf|Emergence of simple-cell receptive field properties by learning a sparse code for natural images]]|| || ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/LeCun98.pdf|Gradient-Based Learning Applied to Document Recognition]]|| || ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Berry1999.pdf|Matrices, Vector Spaces, and Information Retrieval]]|| || ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Drucker1999.pdf|Support vector machines for spam categorization]]|| || ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Mika_1999_Proceedings.pdf|Kernel PCA and De--Noising in Feature Spaces]]|| || ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Hofmann2001.pdf|Unsupervised Learning by Probabilistic Latent Semantic Analysis]]|| || ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Viola2001.pdf|Rapid Object Detection using a Boosted Cascade of Simple Features]]|| || ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/DeBie2004.pdf|Kernel methods for exploratory pattern analysis: a demonstration on text data]]|| || ||
|| [[ http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Blankertz_2007_Neuroimage.pdf| The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects]]|| ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Kamitani_2005.pdf|Decoding the visual and subjective contents of the human brain]]|| ||