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Departments >> Faculty of Engineering >> Department of Electrical-Electronic-Communication Engineering >> Chair of Multimedia Communications and Signal Processing (Prof. Dr. Kaup) >>
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Machine Learning in Signal Processing (MLISP)- Lecturer
- PD Dr.-Ing. Jürgen Seiler, Akad. ORat
- Details
- Vorlesung
Online
3 cred.h, ECTS studies, ECTS credits: 5
nur Fachstudium, Sprache Englisch
Zeit:
- Fields of study
- 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)
- 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.
- Recommended literature
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- ECTS information:
- 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
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- Additional information
- Expected participants: 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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