Lehrende/r

Lehrende/r

• Dipl.Ing. Dr. Ahmad Haj Mosa, Bakk.

LV-Titel englisch

LV-Titel englisch Lab on Machine Learning and Applications in Intelligent Vehicles

LV-Art

LV-Art Kurs (prüfungsimmanente LV )

Semesterstunde/n

Semesterstunde/n 2.0

ECTS-Anrechnungspunkte

ECTS-Anrechnungspunkte 3.0

Anmeldungen

Anmeldungen 12 (20 max.)

Organisationseinheit

Organisationseinheit

• Institut für Intelligente Systemtechnologien

Unterrichtssprache

Unterrichtssprache Englisch

LV-Beginn

LV-Beginn 07.03.2017

eLearning

eLearning zum Moodle-Kurs

Anmerkungen

Anmerkungen

This course will focus on the recent advanced machine learning techniques including: data processing, dimension reduction, deep learning and deep reinforcement learning. 

In the field of transportation and autonomous vehicles,  deep Learning has become a cutting edge technique. Therefore this course will focus deeply on the IMPLEMENTATION following recent techniques:

1. ConvNets https://www.youtube.com/watch?v=URmxzxYlmtg

2. Long term short term memory network https://www.youtube.com/watch?v=FsVSZpoUdSU

3. Cellular Neural Networks

4. Memory Networks 

https://arxiv.org/abs/1605.06065

5. Deep Q network

https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf

In this course we will be using Python with the two most common machine learning tools: sklearn and google tensorflow(https://www.youtube.com/watch?v=mWl45NkFBOc). During this course, students will be ask to do some challenging machine learning task in the frame of self driving cars (check the link https://www.youtube.com/watch?v=Gy69zmKPpD4).

It is mandatory to take the parallel lab course (

700.340 (17S) Machine Learning in Intelligent Transportation

) to master the techniques that will learned during the course

Beurteilungsschema

• 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 on Machine Learning and Applications in Intelligent Vehicles (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 on Machine Learning and Applications in Intelligent Vehicles (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 on Machine Learning and Applications in Intelligent Vehicles (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 on Machine Learning and Applications in Intelligent Vehicles (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 on Machine Learning and Applications in Intelligent Vehicles (2.0h KS / 3.0 ECTS)

• Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
  • Fach: Autonomous Systems and Robotics (WI) (Wahlfach)
    • Wahl aus dem LV-Katalog (siehe Anhang 3) ( 0.0h VK, VO / 30.0 ECTS)
      • 700.373 Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h KS / 3.0 ECTS)

• Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
  • Fach: Free Electives (Freifach)
    • Free Electives ( 0.0h XX / 6.0 ECTS)
      • 700.373 Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h KS / 3.0 ECTS)

• Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
  • Fach: Technischer Schwerpunkt (Intelligent Transportation Systems) (Pflichtfach)
    • 1.4-1.5 Kurs oder Labor ( 4.0h KU / 6.0 ECTS)
      • 700.373 Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h KS / 3.0 ECTS)

• Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
  • Fach: Technischer Schwerpunkt (Media Engineering) (Pflichtfach)
    • 1.4-1.5 Kurs oder Labor ( 4.0h KU / 6.0 ECTS)
      • 700.373 Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h KS / 3.0 ECTS)

• Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
  • Fach: Technische Ergänzung II (Pflichtfach)
    • 3.4-3.5 Kurs oder Labor ( 4.0h KU / 6.0 ECTS)
      • 700.373 Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h KS / 3.0 ECTS)

• Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
  • Fach: Freie Wahlfächer (Freifach)
    • Diverse Lehrveranstaltungen ( 0.0h VO/VK/VS/KU/PS / 12.0 ECTS)
      • 700.373 Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h KS / 3.0 ECTS)