700.340 (24S) Neurocomputing in Robotics and Intelligent Transportation

Sommersemester 2024

Ende der Anmeldefrist
23.05.2024 23:59

Erster Termin der LV
13.04.2024 10:00 - 13:00 Online Off Campus
Nächster Termin:
04.05.2024 10:00 - 14:00 Online Off Campus

Überblick

Lehrende/r
LV-Titel englisch Neurocomputing in Robotics and Intelligent Transportation
LV-Art Vorlesung-Seminar (prüfungsimmanente LV )
LV-Modell Blended-Learning-Lehrveranstaltung
Online-Anteil 40%
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 4.0
Anmeldungen 15 (20 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 13.04.2024
eLearning zum Moodle-Kurs
Seniorstudium Liberale Ja

Zeit und Ort

Liste der Termine wird geladen...

LV-Beschreibung

Intendierte Lernergebnisse

Recent advancements in machine learning methods based on deep neural networks have led to performance breakthroughs in many fields, especially within transportation systems and autonomous vehicles. This class will explore how these technologies are revolutionizing the fields of robotics and transportation, equipping students with the knowledge to contribute to the advancement of smart navigation systems and the development of 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.

Through hands-on projects, participants will also learn to apply deep learning techniques to real-world transportation challenges, preparing them for the future of intelligent mobility and robotic autonomy.

Lehrmethodik inkl. Einsatz von eLearning-Tools

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 Robotics and 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.

Prüfungsmethode/n

Format 

  • An oral exam using a digital platform (e.g., BBB via Moodle) or a written one.

Scheduling

  • Students will be given a specific time slot at least one week before the exam date.
  •  It is crucial for students to check their technical setup before the exam day to avoid any technical difficulties

Prüfungsinhalt/e

  • Review course materials, including lecture notes, readings, and assignments.
  • Think critically about the topics and anticipate potential counterarguments or challenges.


Beurteilungskriterien/-maßstäbe

Evaluation Criteria

  • Depth of Understanding: Demonstrated thorough comprehension of course content
  • Ability to connect course concepts to real-world applications or personal experiences.

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Masterstudium Artificial Intelligence and Cybersecurity (SKZ: 993, Version: 20W.1)
    • Fach: Specialisation in Artificial Intelligence and Cybersecurity (Wahlfach)
      • Fachlich relevante Lehrveranstaltungen ( 0.0h XX / 34.0 ECTS)
        • 700.340 Neurocomputing in Robotics and Intelligent Transportation (2.0h VS / 4.0 ECTS)
          Absolvierung im 2., 3. Semester empfohlen
  • 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 Neurocomputing in Robotics and 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 Neurocomputing in Robotics and 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 Neurocomputing in Robotics and 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 Neurocomputing in Robotics and 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 Neurocomputing in Robotics and 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 Neurocomputing in Robotics and 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 Neurocomputing in Robotics and Intelligent Transportation (2.0h VS / 4.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2023
  • 700.340 VS Machine Learning in 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)