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Machine Learning in Signal Processing (MLISP)5 ECTS (englische Bezeichnung: Machine Learning in Signal Processing)
(Prüfungsordnungsmodul: Machine Learning in Signal Processing)
Modulverantwortliche/r: Jürgen Seiler Lehrende:
Jürgen Seiler
Startsemester: |
WS 2021/2022 | Dauer: |
1 Semester | Turnus: |
jährlich (WS) |
Präsenzzeit: |
60 Std. | Eigenstudium: |
90 Std. | Sprache: |
Englisch |
Lehrveranstaltungen:
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.
Lernziele und Kompetenzen:
After attending the lecture, students will be able to
understand regression and classification problems
apply PDF estimation algorithms
understand Gaussian mixture models and expectation-maximization
apply principal component analysis and independent component analysis
assess different estimation algorithms
explain the application of machine learning to system identification
apply hidden Markov models
understand different artificial neural network architectures
explain deep learning principles
apply artificial neural networks
devise learning strategies for deep neural networks
assess the application of deep neural networks for speech processing tasks.
Literatur:
Literature:
Weitere Informationen:
Schlüsselwörter: Machine Learning
Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:
- Communications and Multimedia Engineering (Master of Science)
(Po-Vers. 2011 | TechFak | Communications and Multimedia Engineering (Master of Science) | Gesamtkonto | Wahlpflichtmodule | Technische Wahlpflichtmodule | Machine Learning in Signal Processing)
- Communications and Multimedia Engineering (Master of Science)
(Po-Vers. 2011 | TechFak | Communications and Multimedia Engineering (Master of Science) | Gesamtkonto | Wahlmodule | Technische Wahlmodule | Machine Learning in Signal Processing)
Dieses Modul ist daneben auch in den Studienfächern "Advanced Signal Processing & Communications Engineering (Master of Science)", "Computational Engineering (Master of Science)", "Computational Engineering (Rechnergestütztes Ingenieurwesen) (Master of Science)", "Data Science (Master of Science)", "Elektrotechnik, Elektronik und Informationstechnik (Bachelor of Science)", "Information and Communication Technology (Master of Science)", "Informations- und Kommunikationstechnik (Master of Science)", "Mechatronik (Master of Science)", "Wirtschaftsingenieurwesen (Master of Science)" verwendbar. Details
Studien-/Prüfungsleistungen:
Machine Learning in Signal Processing (Prüfungsnummer: 84401)
zugeh. "mein campus"-Prüfung: | - 84401 Machine Learning in Signal Processing (Gewichtung: 100.0 %, Prüfung, Form: schriftlich/mündlich, Drittelnoten (mit 4,3), Dauer: -, 5 ECTS, Prüfung).
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- Prüfungsleistung, Klausur, Dauer (in Minuten): 90, benotet, 5 ECTS
- Anteil an der Berechnung der Modulnote: 100.0 %
- Prüfungssprache: Englisch
- Erstablegung: WS 2021/2022, 1. Wdh.: SS 2022
1. Prüfer: | Jürgen Seiler (100290) |
- Termin: 25.02.2022, 10:00 Uhr, Ort: BASPH
Termin: 06.10.2022
Termin: 06.10.2022
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