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Einrichtungen >> Technische Fakultät (TF) >> Department Elektrotechnik-Elektronik-Informationstechnik (EEI) >> Lehrstuhl für Multimediakommunikation und Signalverarbeitung >>
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Seminar on Selected Topics in Machine Learning (Sem ML)- Dozentinnen/Dozenten
- Prof. Dr. Veniamin Morgenshtern, Kamal Gopikrishnan Nambiar, M. Sc.
- Angaben
- Seminar
2 SWS, benoteter Schein, Anwesenheitspflicht, ECTS-Studium, ECTS-Credits: 2,5, Sprache Englisch
Zeit und Ort: n.V.
- Studienfächer / Studienrichtungen
- WPF ASC-MA ab 1 (ECTS-Credits: 2,5)
WPF CME-MA ab 1 (ECTS-Credits: 2,5)
WPF IuK-BA ab 1 (ECTS-Credits: 2,5)
WPF EEI-MA-INT ab 1
- Voraussetzungen / Organisatorisches
- Prior knowledge in machine learning is a prerequisite for participation. For example, good knowledge of the material covered in the FAU course ‘Machine Learning in Signal Processing’, or any equivalent course, is sufficient.
- Inhalt
- In this seminar, we follow some of the latest research developments in the field of mathematical theory for deep learning, weakly-supervised and unsupervised learning, and design of neural network architectures and loss functions. The seminar provides the students an opportunity to build a deep understanding of the underlying machine learning concepts.
This seminar is designed for Bachelor and Master programs in Electrical Engineering, Electronics and Information Technology (EEI), Information and Communication Technology (IuK), Computational Engineering (CE), Communications and Multimedia Engineering (CME), Advanced Signal Processing and Communications Engineering (ASC) as well as related study programs.It consists of three mandatory meetings: 1st meeting (November 2020): An introduction will be given and the individual topics are assigned to the participants. 2nd meeting (mid December 2020: The participants will give a brief presentation about the status of their work and hints for the final presentation are given. 3rd meeting (beginning of February): Each participant will give a presentation of 25 minutes and submit a report on his/her topic of 10 to 15 pages. The exact dates and format of the meetings will be announced in due time.
All meetings and presentations will be given in English and the reports are expected to be written in English.
- ECTS-Informationen:
- Credits: 2,5
- Prerequisites
- Prior knowledge in machine learning is a prerequisite for participation. For example, good knowledge of the material covered in the FAU course ‘Machine Learning in Signal Processing’, or any equivalent course, is sufficient.
- Contents
- In this seminar, we follow some of the latest research developments in the field of mathematical theory for deep learning, weakly-supervised and unsupervised learning, and design of neural network architectures and loss functions. The seminar provides the students an opportunity to build a deep understanding of the underlying machine learning concepts.
This seminar is designed for Bachelor and Master programs in Electrical Engineering, Electronics and Information Technology (EEI), Information and Communication Technology (IuK), Computational Engineering (CE), Communications and Multimedia Engineering (CME), Advanced Signal Processing and Communications Engineering (ASC) as well as related study programs.It consists of three mandatory meetings: 1st meeting (November 2020): An introduction will be given and the individual topics are assigned to the participants. 2nd meeting (mid December 2020: The participants will give a brief presentation about the status of their work and hints for the final presentation are given. 3rd meeting (beginning of February): Each participant will give a presentation of 25 minutes and submit a report on his/her topic of 10 to 15 pages. The exact dates and format of the meetings will be announced in due time.
All meetings and presentations will be given in English and the reports are expected to be written in English.
- Zusätzliche Informationen
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2020/2021:
- Seminar Ausgewählte Kapitel der Multimediakommunikation und Signalverarbeitung (Sem LMS)
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