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Module: Deep Learning for Communications (40979)

Content

Artificial intelligence and machine learning are experiencing a considerable interest these days and their applications now extend into almost every industry and research domain. Particularly deep learning has led to many recent breakthroughs in various domains including computer vision, speech recognition, and natural language processing. This aim of this seminar is to provide an introduction into the concept of deep learning and to present and discuss its application in communications via student presentations of scientific research papers. These include topics such as neural network-based communication systems, channel modeling via generative adversarial networks, code-design via autoencoders, and many others.


Further Information

Module Components:
One course:

SEM - Deep Learning for Communications
Duration of Module:
One semester (SS)
Type of examination:
Portfolio examination
LP:
3
This module is used in the following module lists:
  • Computer Engineering (Master of Science)
  • Computer Science (Informatik) (Master of Science)
  • Elektrotechnik (Master of Science)
  • Wirtschaftsinformatik / Information Systems Management (Master of Science)
Prerequisites:
Prerequisite for participation to courses are a mathematical background at the level of beginning MS students in Electrical Engineering. A background in deep learning/machine learning is desirable, but a brief recap will be given at the beginning.
LP = Leistungspunkte/Credits

Module Supporting Material

  • T. J. O’Shea and J. Hoydis, "An introduction to deep learning for the physical layer," IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563-575, December 2017
  • I. Goodfellow, Y. Bengio and A. Courville, "Deep Learning" MIT Press, 2017

Zusatzinformationen / Extras

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Auxiliary Functions

COURSE - Deep Learning for Communications

LV-Nr. 34331900 L 005
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Prof. Rafael Schaefer
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