700.604 (23S) Machine Learning for Information and Communication Engineering

Sommersemester 2023

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Erster Termin der LV
19.04.2023 08:00 - 12:00 Z.0.18 On Campus
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Überblick

Lehrende/r
LV-Titel englisch Machine Learning for Information and Communication Engineering
LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
LV-Modell Präsenzlehrveranstaltung
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 4.0
Anmeldungen 7
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 19.04.2023
eLearning zum Moodle-Kurs
Anmerkungen

The class is booked twice x day even though it will be held only once. The hours will be decided during the first lecture. If you have specific requests please write me an email.

Zeit und Ort

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LV-Beschreibung

Intendierte Lernergebnisse

At the end of the course, the student will be able to:

  • distinguish between different types of machine learning problems
  • know which technique to use to solve them and how to implement it
  • understand the mathematical fundamentals of artificial neural networks
  • use them into a deep learning  (DL) framework
  • apply the acquired knowledge to solve information and communication engineering related challenges
  • know the latest DL solutions in the domain of communications

Lehrmethodik

The course will cover important theoretical aspects in details that are typically behind the functioning of most artificial intelligence systems and will use Python to implement the studied algorithms. 

Inhalt/e

Topics covered in the course are the following:

  • Introduction to the course
    • What is machine learning for information and communication engineering (ICE), current applications
  • Fundamentals of machine learning, from problem analysis/formulation to its solution and evaluation
    • Type of data, learning problems, learning techniques and evaluation methods
  • Neural networks
    • Artificial neural networks, back-propagation, gradient descent, activation functions
  • Introduction to deep learning for ICE
    • Relevant network architectures, e.g., CNNs, RNNs, LSTM, Transformers
  • Deep learning applications to ICE
    • Learning to decode
    • Autoencoders for communications
    • Generative adversarial networks for communications
    • Resource allocation


Erwartete Vorkenntnisse

Basics of linear algebra, statistics and programming.

Prüfungsinformationen

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Prüfungsmethode/n

  • Assignments
  • Oral interview

Prüfungsinhalt/e

Assignments are part of the exam. They will be given throughout the course based on the exercises and material presented during the course. Moreover, they have to be handed in within specific deadlines before the final oral exam.

Beurteilungskriterien/-maßstäbe

Assignment grade = A, it will count as 50% of final mark
Oral grade = O, it will count as 50% of final mark

Final grade = 0.5*A + 0.5*O

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.604 Machine Learning for Information and Communication Engineering (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.604 Machine Learning for Information and Communication Engineering (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 22W.1)
    • Fach: Information and Communicatons Enginnering: Supplements (Wahlfach)
      • 1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) ( 0.0h VC, KS / 14.0 ECTS)
        • 700.604 Machine Learning for Information and Communication Engineering (2.0h VC / 4.0 ECTS)

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