700.340 (24S) Neurocomputing in Robotics and Intelligent Transportation
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
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:
- Develop object, detection, recognition, localization, and segmentation models.
- Understand the basics of attention mechanisms in deep learning.
- 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
- Introduction to machine learning
- Shallow Neural Networks And Deep Neural Networks - Overview
- Backpropagation algorithm - Overview
- Convolutional Neural Networks
- Semantic segmentation (U-NET)
- Transfer learning
- Object localization (YOLO)
- Semantic embedding (Overview)
- Zero shot learning
- Few shot Learning (Siamese Neural Network)
- Attention + transformers
- 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
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 BenotungsschemaPosition 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
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
-
Fach: Specialisation in Artificial Intelligence and Cybersecurity
(Wahlfach)
- 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)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- 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)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- 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)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- 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)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- 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)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
-
Fach: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)
- 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)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
- 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)
-
2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: ICE- Supplements
(Wahlfach)
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)
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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)