700.325 (19W) Practical Introduction to Neural Networks and Deep Learning

Wintersemester 2019/20

Ende der Anmeldefrist
04.10.2019 23:59

Erster Termin der LV
07.01.2020 09:30 - 19:30 , V.1.04
Nächster Termin:
08.01.2020 08:30 - 15:30 , M.0.22

Überblick

Lehrende/r
LV-Titel englisch
Practical Introduction to Neural Networks and Deep Learning
LV-Art
Vorlesung-Kurs (prüfungsimmanente LV )
Semesterstunde/n
2.0
ECTS-Anrechungspunkte
4.0
Anmeldungen
9 (20 max.)
Organisationseinheit
Unterrichtssprache
Englisch
mögliche Sprache/n der Leistungserbringung
Deutsch
LV-Beginn
07.01.2020
Studienberechtigungsprüfung
Ja

LV-Beschreibung

Intendierte Lernergebnisse

Neural networks and deep learning have different applications in text categorization, e.g., spam filtering, fraud detection, optical character recognition, machine vision, e.g., face detection, licenses plate recognition, advanced driver assistance systems, natural-language processing, e.g., spoken language understanding, market segmentation, e.g., predict if a customer will get a credit, and  bioinformatics, e.g.,  classify proteins or lipidomes according to their function.

The lecture will cover the practical topics regarding (a) Neural networks and deep learning models, (b) guide to transfer the acquired knowledge to solve classification problems for industry and research, and (c) show some use-cases and interesting applications from the state-of-the-art.

Lehrmethodik

Theory + practical examples (Python)

Inhalt/e

  • Data preprocessing
  • Unsupervised Learning and Clustering
  • Deep Learning (multilayer perceptron, convolutional models, recurrent models)
  • Deep learning libraries (torch, theano, keras, tensorflow...etc.)
  • Time series forecast
  • Evaluation Metrics

Prüfungsinformationen

Beurteilungsschema

Note/Grade Benotungsschema

Position im Curriculum

  • Bachelorstudium Informationstechnik (SKZ: 289, Version: 17W.1)
    • Fach: Informationstechnische Vertiefung (Wahlfach)
      • 10a.3 Wahl von Lehrveranstaltungen ( 0.0h VO/VC/KS/UE / 6.0 ECTS)
        • 700.325 Practical Introduction to Neural Networks and Deep Learning (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)
        • 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.325 Practical Introduction to Neural Networks and Deep Learning (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)
        • 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Autonomous Systems and Robotics: Advanced (ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (siehe Anhang 3) ( 0.0h VK, VO / 30.0 ECTS)
        • 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h VC / 4.0 ECTS)

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