700.340 (21S) Machine Learning in Intelligent Transportation

Sommersemester 2021

Registration deadline has expired.

First course session
04.03.2021 14:00 - 16:00 Online Off Campus
... no further dates known

Overview

Due to the COVID-19 pandemic, it may be necessary to make changes to courses and examinations at short notice (e.g. cancellation of attendance-based courses and switching to online examinations).

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

Please note that the currently displayed dates may be subject to change due to COVID-19 measures.
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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

https://www.deeplearningbook.org/

Examination information

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.

Grading scheme

Grade / Grade grading scheme

Position 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)
  • 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)
  • 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)
  • 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)
  • 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)

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)