700.373 (24S) Lab: Neurocomputing in Robotics and Intelligent Transportation

Sommersemester 2024

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Erster Termin der LV
14.03.2024 10:00 - 11:30 B04.1.02 On Campus
Nächster Termin:
02.05.2024 10:00 - 11:30 B04.2.111 On Campus

Überblick

Lehrende/r
LV-Titel englisch Lab: Neurocomputing in Robotics and Intelligent Transportation
LV-Art Kurs (prüfungsimmanente LV )
LV-Modell Präsenzlehrveranstaltung
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 3.0
Anmeldungen 8 (20 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 14.03.2024
eLearning zum Moodle-Kurs

Zeit und Ort

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LV-Beschreibung

Intendierte Lernergebnisse

This course combines theoretical understanding, hands-on practical experience, collaborative projects, and real-world applications. After a successfull completion of this course, participants are expected to gain both theoretical knowledge and practical skills in the field of neurocomputing as it applies to robotics and intelligent transportation. The main learning objectives are:

  1. Understand the Principles of Neurocomputing:

    • Define and explain the fundamental concepts of neurocomputing, including neural networks, synaptic weights, activation functions, and learning algorithms.
    • Explore the theoretical foundations of neurocomputing and its applications in the context of robotics and intelligent transportation systems.

  2. Implement Neural Networks for Robotics Tasks:

    • Develop practical skills in designing and implementing neural networks for specific robotics applications, such as sensor data processing, path planning, and object recognition.
    • Gain hands-on experience in programming and configuring neural network architectures using popular frameworks like TensorFlow or PyTorch.

  3. Apply Neurocomputing Techniques to Intelligent Transportation Systems:

    • Investigate how neurocomputing can be leveraged to enhance the performance of intelligent transportation systems, including traffic management, vehicle control, and autonomous navigation.
    • Evaluate the advantages and limitations of neurocomputing approaches in addressing real-world challenges in transportation and robotics.

  4. Optimize Neural Networks for Efficiency and Accuracy:

    • Explore techniques for optimizing neural network models to achieve a balance between computational efficiency and accuracy in robotic and transportation applications.
    • Learn about regularization methods, hyperparameter tuning, and model compression to enhance the overall performance of neurocomputing solutions.

  5. Analyze and Interpret Results in a Robotics and Transportation Context:

    • Develop skills in evaluating and interpreting the results of neurocomputing models in the context of robotics and intelligent transportation systems.
    • Discuss the implications of neural network outputs on decision-making processes, safety considerations, and overall system performance.

Lehrmethodik

This course combines theoretical understanding, hands-on practical experience, and collaborative projects. Methodologies that are used are:

  • Interactive Lectures: with multimedia presentations for explaining the theoretical part
  • Hands-on Coding Sessions: for implementing the theory
  • Reflective Discussions: in class for training in critical thinking and digesting better the theory
  • Group Projects: for mastering collaboration
  • Student Presentations: for mastering presenting in front of an audience

Inhalt/e

The main topics of this course are:

  1. Deep Neural Network Fundamentals

  2. Applications of Neurocomputing in Robotics

  3. Intelligent Transportation Systems (Concept & Applications)

  4. Optimisation Techniques for Neural Networks

  5. Ethical Considerations and Societal Impacts

Erwartete Vorkenntnisse

Mandatory:

  • Basic knowledge of Python 
  • Basic knowledge of Deep Learning Concepts
  • Knowledge of Mathematics (Probabilities, Statistics)


Nice to have:

  • Experience in Neural Network Training
  • Experience in reviewing papers

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

Student performance will be assessed through a comprehensive examination method encompassing various engagement and achievement aspects.

  • Class attendance holds 10% of the evaluation, emphasizing the importance of active participation in the learning process. 
  • Homework assignments contribute to 30% of the evaluation, promoting continuous individual learning and 
  • Project presentations hold 60% of the evaluation, promoting the demonstration of mastering the application of theoretical concepts in practical scenarios. 

For passing the course, a student must collect at least 60%.

Prüfungsinhalt/e

Lecture material      

Beurteilungskriterien/-maßstäbe

The assessment of the students will follow these criteria:

  • Active participation in class discussions
  • Homework percentage completion
  • Project completion & quality
  • Project presentation quality

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)
        • 700.373 Lab: Neurocomputing in Robotics and Intelligent Transportation (2.0h KS / 3.0 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)
        • 700.373 Lab: Neurocomputing in Robotics and Intelligent Transportation (2.0h KS / 3.0 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)
        • 700.373 Lab: Neurocomputing in Robotics and Intelligent Transportation (2.0h KS / 3.0 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)
        • 700.373 Lab: Neurocomputing in Robotics and Intelligent Transportation (2.0h KS / 3.0 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)
        • 700.373 Lab: Neurocomputing in Robotics and Intelligent Transportation (2.0h KS / 3.0 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)
        • 700.373 Lab: Neurocomputing in Robotics and Intelligent Transportation (2.0h KS / 3.0 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)
        • 700.373 Lab: Neurocomputing in Robotics and Intelligent Transportation (2.0h KS / 3.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 22W.1)
    • Fach: Freie Wahlfächer (Freifach)
      • 1.6 Freie Wahlfächer ( 0.0h XX / 6.0 ECTS)
        • 700.373 Lab: Neurocomputing in Robotics and Intelligent Transportation (2.0h KS / 3.0 ECTS)
  • Bachelorstudium Robotics and Artificial Intelligence (SKZ: 295, Version: 22W.1)
    • Fach: Robotics & AI Applications (Wahlfach)
      • 8.1 Robotics & AI Applications ( 0.0h VO, VC, UE, KS / 12.0 ECTS)
        • 700.373 Lab: Neurocomputing in Robotics and Intelligent Transportation (2.0h KS / 3.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2023
  • 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
Sommersemester 2022
  • 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
Sommersemester 2021
  • 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
Sommersemester 2020
  • 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
Sommersemester 2019
  • 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
Sommersemester 2018
  • 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
Sommersemester 2017
  • 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
Sommersemester 2016
  • 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
Sommersemester 2015
  • 700.373 KU Labor: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h / 3.0ECTS)
Sommersemester 2014
  • 700.373 KU Labor: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h / 3.0ECTS)
Sommersemester 2013
  • 700.373 KU Labor: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h / 3.0ECTS)