700.340 (21S) Machine Learning in Intelligent Transportation
Overview
For further information regarding teaching on campus, please visit: https://www.aau.at/en/corona.
- Lecturer
- Course title german Machine Learning in Intelligent Transportation
- Type Lecture - Seminar (continuous assessment course )
- Course model Online course
- Hours per Week 2.0
- ECTS credits 4.0
- Registrations 22 (20 max.)
- Organisational unit
- Language of instruction English
- Course begins on 04.03.2021
- eLearning Go to Moodle course
- Seniorstudium Liberale Yes
Time and place
Course Information
Intended learning outcomes
After this course student will get the knowledge about the state of the art methods regarding machine learning and particularly deep learning
Students will be able to model the state of the art neural networks architectures.
Several applications will be addressed such as: Self driving cars, Natural Language Processing, Machine Vision and state of the art Alphazero player
And we will learn how to build state of the art self driving car simulation https://www.youtube.com/watch?v=Fgv5H_Parwc&list=PLUAvE4k0OWwdbKM7G532jd_jowoDmue2o
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
Course content
1. Introduction to machine learning
2. Linear and Logistic regression
3. Shallow Neural Networks
4. Deep Neural Networks
5. Backpropagation algorithms
6. Convolutional Neural Networks
7. Autoencoders
8. Recurrent Neural Networks
9. Long Term Short Term Memory Networks
10. Deep Learning in Natural Language Processing
11. Differentiable Neural Computing
12. Deep Reinforcement Learning
Prior knowledge expected
Applied Statistics + Calculus
Literature
Examination information
Grading scheme
Grade / Grade grading schemePosition in the curriculum
- Masterstudium 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 Machine Learning in 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)
- Masterstudium 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 Machine Learning in 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)
- Masterstudium 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 Machine Learning in 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)
- Masterstudium 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 Machine Learning in 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)
- Masterstudium 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 Machine Learning in 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)
Equivalent courses for counting the examination attempts
-
Sommersemester 2024
- 700.340 VS Neurocomputing in Robotics and Intelligent Transportation (2.0h / 4.0ECTS)
-
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 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)