Machine Learning and Optimization in Communications

  • type: Lecture (V)
  • chair: KIT-Fakultäten - KIT-Fakultät für Elektrotechnik und Informationstechnik
  • semester: SS 2021
  • time: 2021-04-12
    12:00 - 13:30 weekly


    2021-04-16
    10:00 - 11:30 weekly

    2021-04-19
    12:00 - 13:30 weekly

    2021-04-23
    10:00 - 11:30 weekly

    2021-04-26
    12:00 - 13:30 weekly

    2021-04-30
    10:00 - 11:30 weekly

    2021-05-03
    12:00 - 13:30 weekly

    2021-05-07
    10:00 - 11:30 weekly

    2021-05-10
    12:00 - 13:30 weekly

    2021-05-14
    10:00 - 11:30 weekly

    2021-05-17
    12:00 - 13:30 weekly

    2021-05-21
    10:00 - 11:30 weekly

    2021-05-31
    12:00 - 13:30 weekly

    2021-06-04
    10:00 - 11:30 weekly

    2021-06-07
    12:00 - 13:30 weekly

    2021-06-11
    10:00 - 11:30 weekly

    2021-06-14
    12:00 - 13:30 weekly

    2021-06-18
    10:00 - 11:30 weekly

    2021-06-21
    12:00 - 13:30 weekly

    2021-06-25
    10:00 - 11:30 weekly

    2021-06-28
    12:00 - 13:30 weekly

    2021-07-02
    10:00 - 11:30 weekly

    2021-07-05
    12:00 - 13:30 weekly

    2021-07-09
    10:00 - 11:30 weekly

    2021-07-12
    12:00 - 13:30 weekly

    2021-07-16
    10:00 - 11:30 weekly

    2021-07-19
    12:00 - 13:30 weekly

    2021-07-23
    10:00 - 11:30 weekly


  • lecturer: Prof. Dr.-Ing. Laurent Schmalen
  • sws: 2
  • lv-no.: 2310560
  • information: Online

With machine learning and deep learning, artificial intelligence has entered nearly every field of engineering in the recent past. This lecture aims to teach the fundamentals of the mechanisms behind machine-/ deep learning and numerical optimizations in the context of communication engineering. Solutions to challenges of modern communication systems with the given techniques are discussed and the application of the given tools is presented.
It is also shown how this knowledge can be transfered to other domains of (electrical) engineering.

 

 

Topics:

  • Fundamentals of artificial intelligence, machine learning and deep learning
  • Training of deep neural networks with state-of-the-art software toolboxes and packages (e.g., TensorFlow)
  • Application of machine learning to design advanced communication systems
  • Reinforcement learning to operate communication systems
  • Fundamentals of numerical optimization
  • Use of convex optimization in telecommunications
  • Application of genetic algorithms
  • Discussion of current research topics and applications