UnivIS
Informationssystem der Friedrich-Alexander-Universität Erlangen-Nürnberg © Config eG 

Machine Learning in Signal Processing (MLISP)

Dozent/in
PD Dr.-Ing. Jürgen Seiler, Akad. ORat

Angaben
Vorlesung
Online
3 SWS, ECTS-Studium, ECTS-Credits: 5
nur Fachstudium, Sprache Englisch
Zeit:

Studienfächer / Studienrichtungen
WF EEI-BA ab 5
PF ASC-MA 1-4 (ECTS-Credits: 5)
WPF CME-MA 1-4 (ECTS-Credits: 5)
WPF ICT-MA-ES 1-4 (ECTS-Credits: 5)
WPF ICT-MA-MPS 1-4 (ECTS-Credits: 5)
WPF ICT-MA-NDC 1-4 (ECTS-Credits: 5)
WPF CE-MA-TA-IT 1-4 (ECTS-Credits: 5)
WF EEI-MA ab 1 (ECTS-Credits: 5)
WPF DS-BA-MSD ab 1 (ECTS-Credits: 5)

Inhalt
This course is an introduction into machine learning and artificial intelligence. The special emphasis is on applications to modern signal processing problems. The course is focused on design principles of machine learning algorithms. The lectures start with a short introduction, where the nomenclature is defined. After this, probabilistic graphical models are introduced and the use of latent variables is discussed, concluding with a discussion of hidden Markov models and Markov fields. The second part of the course is about deep learning and covers the use of deep neural networks for machine learning tasks. In the last part of the lecture, the use of deep neural networks for speech processing tasks is introduced.
The course is based on the materials and video footage from Dr. Roland Maas. He is an outstanding machine learning expert and a former member of the Chair of Multimedia Communications and Signal Processing.

Empfohlene Literatur
  • C. M. Bishop: Pattern Recognition and Machine Learning, http://www.research.microsoft.com/en-us/um/people/cmbishop/PRML

  • S. Theodoridis and K. Koutroumbas: Pattern Recognition

  • M. Nielsen: Neural Networks and Deep Learning.

ECTS-Informationen:
Title:
Machine Learning in Signal Processing

Credits: 5

Contents
This course is an introduction into machine learning and artificial intelligence. The special emphasis is on applications to modern signal processing problems. The course is focused on design principles of machine learning algorithms. The lectures start with a short introduction, where the nomenclature is defined. After this, probabilistic graphical models are introduced and the use of latent variables is discussed, concluding with a discussion of hidden Markov models and Markov fields. The second part of the course is about deep learning and covers the use of deep neural networks for machine learning tasks. In the last part of the lecture, the use of deep neural networks for speech processing tasks is introduced.
The course is based on the materials and video footage from Dr. Roland Maas. He is an outstanding machine learning expert and a former member of the Chair of Multimedia Communications and Signal Processing.

Literature
  • C. M. Bishop: Pattern Recognition and Machine Learning, http://www.research.microsoft.com/en-us/um/people/cmbishop/PRML

  • S. Theodoridis and K. Koutroumbas: Pattern Recognition

  • M. Nielsen: Neural Networks and Deep Learning.

Zusätzliche Informationen
Erwartete Teilnehmerzahl: 17, Maximale Teilnehmerzahl: 80
www: https://www.studon.fau.de/crs1945222.html

Verwendung in folgenden UnivIS-Modulen
Startsemester WS 2021/2022:
Machine Learning in Signal Processing (MLISP)
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