700.395 (13W) Data Mining in Intelligent Transportation and Logistics

Wintersemester 2013/14

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Erster Termin der LV
01.10.2013 14:00 - 16:00 L4.1.02 ICT-Lab Off Campus
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Überblick

Lehrende/r
LV-Titel englisch Data Mining in Intelligent Transportation and Logistics
LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 4.0
Anmeldungen 17 (25 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 01.10.2013
Seniorstudium Liberale Ja

Zeit und Ort

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LV-Beschreibung

Inhalt/e

The goal of this course is to give the students the algorithmic methods at the heart of successful data mining-including tried and true techniques. Finally, some concrete applications of practical interest will be provided.

Themen

  • * Introduction to MATLAB
  • * Mathematical basics
  • * Data selection and preparation
  • * Bayesian classifier
  • * Linear models
  • * Non linear models
  • * Prediction (Hidden Markov Models)
  • * Time Series Analysis
  • * Collaborative filtering
  • * Optimization metrics
  • * Evaluation Metrics

Lehrziel

* Traffic data analysis (cameras and microphones for vehicle and pedestrian detection). * Traffic Flow prediction, Traffic speed prediction, Traffic sign recognition. * Complex traffic patterns analysis (shock waves and adaptive traffic control). * Driver behavior analysis based on different types of sensors.

Erwartete Vorkenntnisse

None

Literatur

Data Mining: Practical Machine Learning Tools and Techniques # Publisher: Morgan Kaufmann; 2 edition (June 22, 2005) # Ian H. Witten, Eibe Frank # Language: English # ISBN-10: 0120884070 # ISBN-13: 978-0120884070 Principles of Neuro Computing for Science & Engineering # Fredric M. Ham # Ivica Kostanic # ISBN: 0-07-025966-6 # Publisher: McGraw-Hill George Papadourakis, “Introduction to Neural Networks”, Lecture Notes

Inhalt/e

The goal of this course is to give the students the algorithmic methods at the heart of successful data mining-including tried and true techniques. Finally, some concrete applications of practical interest will be provided.

Themen

  • * Introduction to MATLAB
  • * Mathematical basics
  • * Data selection and preparation
  • * Bayesian classifier
  • * Linear models
  • * Non linear models
  • * Prediction (Hidden Markov Models)
  • * Time Series Analysis
  • * Collaborative filtering
  • * Optimization metrics
  • * Evaluation Metrics

Lehrziel

* Traffic data analysis (cameras and microphones for vehicle and pedestrian detection). * Traffic Flow prediction, Traffic speed prediction, Traffic sign recognition. * Complex traffic patterns analysis (shock waves and adaptive traffic control). * Driver behavior analysis based on different types of sensors.

Erwartete Vorkenntnisse

None

Literatur

Data Mining: Practical Machine Learning Tools and Techniques # Publisher: Morgan Kaufmann; 2 edition (June 22, 2005) # Ian H. Witten, Eibe Frank # Language: English # ISBN-10: 0120884070 # ISBN-13: 978-0120884070 Principles of Neuro Computing for Science & Engineering # Fredric M. Ham # Ivica Kostanic # ISBN: 0-07-025966-6 # Publisher: McGraw-Hill George Papadourakis, “Introduction to Neural Networks”, Lecture Notes

Prüfungsinformationen

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.

Beurteilungskriterien/-maßstäbe

Written exam + Project

Beurteilungskriterien/-maßstäbe

Written exam + Project

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technischer Schwerpunkt (Intelligent Transportation Systems) (Pflichtfach)
      • 1.1-1.3 Vorlesung mit Kurs oder Vorlesung mit Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.395 Data Mining in Intelligent Transportation and Logistics (2.0h VK / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technischer Schwerpunkt (Media Engineering) (Pflichtfach)
      • 1.1-1.3 Vorlesung mit Kurs oder Vorlesung mit Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.395 Data Mining in Intelligent Transportation and Logistics (2.0h VK / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technische Ergänzung II (Pflichtfach)
      • 3.1-3.3 Vorlesung mit Kurs oder Vorlesung mit Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.395 Data Mining in Intelligent Transportation and Logistics (2.0h VK / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Research Track (Methodischer Schwerpunkt) (Pflichtfach)
      • 4.2'-4.3' Theoretisch-Methodische Lehrveranstaltung I/II ( 0.0h VO/VK/VS/KU/PS / 6.0 ECTS)
        • 700.395 Data Mining in Intelligent Transportation and Logistics (2.0h VK / 4.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Wintersemester 2023/24
  • 700.395 VC Data Mining, Synthetic Data, and Knowledge Discovery (2.0h / 4.0ECTS)
Wintersemester 2022/23
  • 700.395 VC Data Mining, Synthetic Data and Knowledge Discovery (2.0h / 4.0ECTS)
Wintersemester 2021/22
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Wintersemester 2020/21
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Sommersemester 2020
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Sommersemester 2019
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Wintersemester 2017/18
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Wintersemester 2016/17
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Wintersemester 2015/16
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Wintersemester 2012/13
  • 700.395 VK Data Mining in Intelligent Transportation and Logistics (2.0h / 4.0ECTS)