700.340 (24S) Neurocomputing in Robotics and Intelligent Transportation
Overview
- Lecturer
- Course title german Neurocomputing in Robotics and Intelligent Transportation
- Type Lecture - Seminar (continuous assessment course )
- Course model Blended learning course
- Online proportion 40%
- Hours per Week 2.0
- ECTS credits 4.0
- Registrations 15 (20 max.)
- Organisational unit
- Language of instruction English
- Course begins on 13.04.2024
- eLearning Go to Moodle course
- Seniorstudium Liberale Yes
Time and place
Course Information
Intended learning outcomes
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.
Teaching methodology including the use of eLearning tools
The sessions will combine theoretical background, seminars about recent papers and mathematical modeling of deep learning architectures focusing on convolutionAl neural networks.
Course content
- 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
Prior knowledge expected
Applied Statistics + calculus + coding skills in python
Literature
Python Machine Learning, 3rd edition. By Raschka & Mirjalili
Examination information
Examination methodology
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
Examination topic(s)
- Review course materials, including lecture notes, readings, and assignments.
- Think critically about the topics and anticipate potential counterarguments or challenges.
Assessment criteria / Standards of assessment for examinations
Evaluation Criteria
- Depth of Understanding: Demonstrated thorough comprehension of course content
- Ability to connect course concepts to real-world applications or personal experiences.
Grading scheme
Grade / Grade grading schemePosition in the curriculum
- Master's degree programme Artificial Intelligence and Cybersecurity
(SKZ: 993, Version: 20W.1)
-
Subject: Specialisation in Artificial Intelligence and Cybersecurity
(Compulsory elective)
-
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)
-
Subject: Specialisation in Artificial Intelligence and Cybersecurity
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Subject: Information and Communications Engineering: Supplements (NC, ASR)
(Compulsory elective)
-
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)
-
Subject: Information and Communications Engineering: Supplements (NC, ASR)
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
-
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)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Subject: Information and Communications Engineering: Supplements (NC, ASR)
(Compulsory elective)
-
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)
-
Subject: Information and Communications Engineering: Supplements (NC, ASR)
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
-
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)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Subject: Autonomous Systems and Robotics: Advanced (ASR)
(Compulsory elective)
-
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)
-
Subject: Autonomous Systems and Robotics: Advanced (ASR)
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Subject: Information and Communicatons Enginnering: Supplements
(Compulsory elective)
-
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)
-
Subject: Information and Communicatons Enginnering: Supplements
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Subject: ICE- Supplements
(Compulsory elective)
-
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)
-
Subject: ICE- Supplements
(Compulsory elective)
Equivalent courses for counting the examination attempts
-
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)