700.340 (20S) Machine Learning in Intelligent Transportation

Sommersemester 2020

Anmeldefrist abgelaufen.

Erster Termin der LV
03.04.2020 08:00 - 16:30 , B04.1.02 ICT-Labor
... keine weiteren Termine bekannt

Überblick

Lehrende/r
LV-Titel englisch
Machine Learning in Intelligent Transportation
LV-Art
Vorlesung-Seminar (prüfungsimmanente LV )
Semesterstunde/n
2.0
ECTS-Anrechungspunkte
4.0
Anmeldungen
11 (20 max.)
Organisationseinheit
Unterrichtssprache
Englisch
LV-Beginn
03.04.2020
eLearning
zum Moodle-Kurs
Anmerkungen

After this course student will get the knowledge about the state of the art methods regarding machine learning and particularly deep learning

Students will be able to model the state of the art neural networks architectures.

Several applications will be addressed such as: Self driving cars, Natural Language Processing, Machine Vision and state of the art Alphazero player

Studienberechtigungsprüfung
Ja
Seniorstudium Liberale
Ja

Zeit und Ort

LV-Beschreibung

Intendierte Lernergebnisse

After this course student will get the knowledge about the state of the art methods regarding machine learning and particularly deep learning

Students will be able to model the state of the art neural networks architectures.

Several applications will be addressed such as: Self driving cars, Natural Language Processing, Machine Vision and state of the art Alphazero player

And we will learn how to build state of the art self driving car simulation https://www.youtube.com/watch?v=Fgv5H_Parwc&list=PLUAvE4k0OWwdbKM7G532jd_jowoDmue2o

Lehrmethodik

The sessions will combine theoretical background, seminars about recent papers and mathematical modeling of deep learning architectures  


Inhalt/e

1. Introduction to machine learning

2. Linear and Logistic regression

3. Shallow Neural Networks

4. Deep Neural Networks

5. Backpropagation algorithms

6. Convolutional Neural Networks

7. Autoencoders

8. Recurrent Neural Networks

9. Long Term Short Term Memory Networks

10. Deep Learning in Natural Language Processing 

11. Differentiable Neural Computing

12. Deep Reinforcement Learning 




Erwartete Vorkenntnisse

Applied Statistics + Calculus


Literatur

https://www.deeplearningbook.org/

Prüfungsinformationen

Prüfungsmethode/n

written exam

Prüfungsinhalt/e

contents of the lecture

Beurteilungskriterien/-maßstäbe

written exam

Beurteilungsschema

Note/Grade 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)
        • 700.340 Machine Learning in Intelligent Transportation (2.0h VS / 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)
        • 700.340 Machine Learning in Intelligent Transportation (2.0h VS / 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)
        • 700.340 Machine Learning in Intelligent Transportation (2.0h VS / 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)
        • 700.340 Machine Learning in Intelligent Transportation (2.0h VS / 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)
        • 700.340 Machine Learning in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technischer Schwerpunkt (Intelligent Transportation Systems) (Pflichtfach)
      • 1.1-1.3 Vorlesung mit Kurs oder Vorlesung mit Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.340 Machine Learning in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technische Ergänzung I (Pflichtfach)
      • 2.1-2.2 Vorlesung mit Kurs oder Vorlesung mit Seminar ( 4.0h VK/VS / 8.0 ECTS)
        • 700.340 Machine Learning in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technische Ergänzung II (Pflichtfach)
      • 3.1-3.3 Vorlesung mit Kurs oder Vorlesung mit Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.340 Machine Learning in Intelligent Transportation (2.0h VS / 4.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2019
  • 700.340 VS Machine Learning in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2018
  • 700.340 VS Machine Learning in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2017
  • 700.340 VS Machine Learning in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2016
  • 700.340 VS Machine Vision in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2015
  • 700.340 VS Machine Vision in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2014
  • 700.340 VS Machine Vision in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2013
  • 700.340 VS Machine Vision in Intelligent Transportation (2.0h / 4.0ECTS)