Aktuelle Seite:
Seminar: Independent study course: Deep learning - Details

  • Detaillierte Informationen über die Veranstaltung werden angezeigt, wie z.B. die Veranstaltungsnummer, Zuordnungen, DozentInnen, TutorInnen etc. In den Detail-Informationen ist unter Aktionen das Eintragen in eine Veranstaltung möglich.

  • link-extern Weiterführende Hilfe
Sie sind nicht angemeldet.

Independent study course: Deep learning

Allgemeine Informationen

Veranstaltungsnummer 8.3233
Semester SS 2015
Aktuelle Anzahl der Teilnehmenden 44
Heimat-Einrichtung LE Cognitive Science
Veranstaltungstyp Seminar in der Kategorie Offizielle Lehrveranstaltungen
Erster Termin Fr , 24.04.2015 10:00 - 12:00, Ort: 93/E12
Teilnehmende ab 4. Semester
Sprache Englisch
Contact Hours 2
ECTS-Punkte 4



Donnerstag: 10:00 - 12:00, wöchentlich (ab 07.05.2015), Reading Club, Ort: 93/E12, 93/E15
Freitag: 10:00 - 12:00, wöchentlich (ab 24.04.2015), Ort: 93/E12, 93/E01, 93/E09
Termine am Donnerstag. 30.07., Donnerstag. 10.09., Donnerstag. 17.09., Donnerstag. 24.09. 10:00 - 12:00, Ort: 93/E12


93/E12 Do. 10:00 - 12:00 (6x)
Fr. 10:00 - 12:00 (8x)
Donnerstag. 30.07., Donnerstag. 10.09., Donnerstag. 17.09., Donnerstag. 24.09. 10:00 - 12:00
93/E15 Do. 10:00 - 12:00 (3x)
93/E01 Fr. 10:00 - 12:00 (1x)
93/E09 Fr. 10:00 - 12:00 (1x)



Prerequisites: There are no hard requirements to attend this course, although some background in machine learning will not harm.

Deep learning is a recent branch in the field of machine learning that is driven by the idea of nested hierarchies, allowing to learn complex structures as combination of simpler ones. Under this label various existing techniques from supervised and unsupervised learning have been adapted yielding new deep architectures, like deep neural networks, deep Boltzmann machines, and deep coding machines to name just a few. Deep learning systems have been successfully applied to a broad spectrum of tasks like image and speech recognition, natural language processing, information retrieval, and multimodal learning.

The course will start by acquiring an overview of practices, trends, and research in the field of deep learning. This will serve as a basis to study selected applications in different areas. In a practical part, students are invited to apply the new knowledge to some task of their choice.