700.340 (23S) Machine Learning in Intelligent Transportation

Sommersemester 2023

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Erster Termin der LV
05.05.2023 09:00 - 17:00 Online Off Campus
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Überblick

Lehrende/r
LV-Titel englisch Machine Learning in Intelligent Transportation
LV-Art Vorlesung-Seminar (prüfungsimmanente LV )
LV-Modell Präsenzlehrveranstaltung
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 4.0
Anmeldungen 11 (20 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 05.05.2023
eLearning zum Moodle-Kurs
Seniorstudium Liberale Ja

Zeit und Ort

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LV-Beschreibung

Intendierte Lernergebnisse

Recent advancements in machine learning methods based on deep neural networks have led to performance breakthroughs in many fields, especially transportation systems and autonomous vehicles.

This class will not only go over the principles of deep learning but also provide students with an understanding of the most recent research in the field. The first part of the class will begin with a high-level introduction to neural models.

After that, the fundamental strategies and cutting-edge methods for constructing, training, and visually depicting different types of deep learning models to:

  1. Develop object, detection, recognition, localization, and segmentation models.
  2. Understand the basics of attention mechanisms in deep learning.
  3. Overcome the "lack of training data" problem using zero and few-shot learning techniques.

Methods based on convolutional neural networks will be discussed as they pertain to the spatial localization of visual items seen. After this course, students can model state-of-the-art advanced deep learning models. Several applications will be addressed, such as Self-driving cars, Machine Vision, and state-of-the-art intelligent transportation systems.

Lehrmethodik

The sessions will combine theoretical background, seminars about recent papers and mathematical modeling of deep learning architectures  focusing on convolutionAl neural networks.


Inhalt/e

  1. Introduction to machine learning
  2. Shallow Neural Networks And Deep Neural Networks - Overview
  3. Backpropagation algorithm - Overview
  4. Convolutional Neural Networks
  5. Semantic segmentation (U-NET)
  6. Transfer learning
  7. Object localization (YOLO)
  8. Semantic embedding (Overview)
  9. Zero shot learning 
  10. Few shot Learning (Siamese Neural Network)
  11. Attention + transformers 
  12. Application in Intelligent Transportation

                          






Erwartete Vorkenntnisse

Applied Statistics + calculus + coding skills in python 


Literatur

Python Machine Learning, 3rd edition. By Raschka & Mirjalili

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.

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)
        • 700.340 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 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)
        • 700.340 Machine Learning in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 22W.1)
    • Fach: ICE- Supplements (Wahlfach)
      • 2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) ( 0.0h VC, KS / 14.0 ECTS)
        • 700.340 Machine Learning in Intelligent Transportation (2.0h VS / 4.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2024
  • 700.340 VS Neurocomputing in Robotics and Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2022
  • 700.340 VS Machine Learning in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2021
  • 700.340 VS Machine Learning in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2020
  • 700.340 VS Machine Learning in Intelligent Transportation (2.0h / 4.0ECTS)
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)