700.370 (15S) Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies

Sommersemester 2015

Registration deadline has expired.

First course session
02.03.2015 10:00 - 12:00 L4.1.01 Off Campus
... no further dates known

Overview

Lecturer
Course title german Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies
Type Seminar (continuous assessment course )
Hours per Week 2.0
ECTS credits 4.0
Registrations 11 (20 max.)
Organisational unit
Language of instruction English
Course begins on 02.03.2015

Time and place

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Course Information

Course content

In the lecture we deal with various aspects of pattern recognition and their applications in Image processing for INTELLIGENT VEHICLES TECHNOLOGIES and ROBOTICS. Different classification methods for both statistical and stochastical approaches will be presented.

Topics

  • * Introduction/Overview to Pattern Recognition
  • * Feature Selection ( Outlier Removal, Data Normalization , Missing data, Uncertainty Handling )
  • * Feature Generation ( Principal components analysis , Kernel Principal components analysis)
  • * Clustering (Expectation Maximization, Nearest Neighbor, k-means , Self-organized maps)
  • * Supervised Learning (Perceptron, Perceptron learning algorithm, Multi-layered neural networks, back propagation, Linear Models, Nonlinear Models)
  • * Context Dependent Classification (Hidden Markov Models, Bayes Classifier)
  • * Model Evaluation
  • * Deep Learning (Convolutional Neural Network, Deep Belief Network, Auto encoder, Sparse Encoder, Boltzmann Machine)
  • * Introduction to Advanced Driver assistance Systems (ADAS)
  • * Applications in Image processing / machine Vision
  • * Introduction to ROBOTICS

Keywords

Big Data

Teaching objective

* Advanced analysis skills and complex problem solving (Big Data) * Object detection and recognition * Advanced applications of artificial intelligence (audio/video patterns recognition)

Literature

Based on the books: * Pattern Recognition - Sergios Theodoridis * Data Mining: Practical Machine Learning Tools and Techniques - Ian H. Witten, Eibe Frank

Course content

In the lecture we deal with various aspects of pattern recognition and their applications in Image processing for INTELLIGENT VEHICLES TECHNOLOGIES and ROBOTICS. Different classification methods for both statistical and stochastical approaches will be presented.

Topics

  • * Introduction/Overview to Pattern Recognition
  • * Feature Selection ( Outlier Removal, Data Normalization , Missing data, Uncertainty Handling )
  • * Feature Generation ( Principal components analysis , Kernel Principal components analysis)
  • * Clustering (Expectation Maximization, Nearest Neighbor, k-means , Self-organized maps)
  • * Supervised Learning (Perceptron, Perceptron learning algorithm, Multi-layered neural networks, back propagation, Linear Models, Nonlinear Models)
  • * Context Dependent Classification (Hidden Markov Models, Bayes Classifier)
  • * Model Evaluation
  • * Deep Learning (Convolutional Neural Network, Deep Belief Network, Auto encoder, Sparse Encoder, Boltzmann Machine)
  • * Introduction to Advanced Driver assistance Systems (ADAS)
  • * Applications in Image processing / machine Vision
  • * Introduction to ROBOTICS

Teaching objective

* Advanced analysis skills and complex problem solving (Big Data) * Object detection and recognition * Advanced applications of artificial intelligence (audio/video patterns recognition)

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.

Assessment criteria / Standards of assessment for examinations

Project 50% + Homeworks 50%

Grading scheme

Grade / Grade grading scheme

Position in the curriculum

  • Bachelor's degree programme Information Technology (SKZ: 289, Version: 12W.2)
    • Subject: Bachelorarbeit, Studienzweig Ingenieurwissenschaften (Compulsory subject)
      • Seminar aus dem Bereich Ingenieurwissenschaften ( 2.0h SE / 3.0 ECTS)
        • 700.370 Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies (2.0h SE / 3.0 ECTS)
          Absolvierung im 6. Semester empfohlen
  • Bachelor's degree programme Information Technology (SKZ: 289, Version: 09W.2)
    • Subject: Bachelorarbeit und Seminar (Compulsory subject)
      • Seminar (zur Bachelorarbeit) ( 2.0h SE / 3.0 ECTS)
        • 700.370 Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies (2.0h SE / 3.0 ECTS)
  • Bachelor's degree programme Information Technology (SKZ: 289, Version: 06W.1)
    • Subject: Compulsory Elective Courses in Information Technology (Compulsory elective)
      • Seminar ( 2.0h SE / 4.0 ECTS)
        • 700.370 Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies (2.0h SE / 4.0 ECTS)
  • Master's degree programme Information Technology (SKZ: 489, Version: 06W.3)
    • Subject: Major Field of Specialization (Intelligent Transportation Systems) (Compulsory subject)
      • 1.1-1.3 Lecture with Exercises or Lecture with Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.370 Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies (2.0h SE / 4.0 ECTS)
  • Master's degree programme Information Technology (SKZ: 489, Version: 06W.3)
    • Subject: Major Field of Specialization (Media Engineering) (Compulsory subject)
      • 1.1-1.3 Lecture with Exercises or Lecture with Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.370 Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies (2.0h SE / 4.0 ECTS)
  • Master's degree programme Information Technology (SKZ: 489, Version: 06W.3)
    • Subject: Additional Technical Module I (Compulsory subject)
      • 2.3 Lecture with Exercises or Seminar ( 2.0h VK/SE / 4.0 ECTS)
        • 700.370 Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies (2.0h SE / 4.0 ECTS)
  • Master's degree programme Information Technology (SKZ: 489, Version: 06W.3)
    • Subject: Additional Technical Module II (Compulsory subject)
      • 3.1-3.3 Lecture with Exercises or Lecture with Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.370 Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies (2.0h SE / 4.0 ECTS)
  • Master's degree programme Information Technology (SKZ: 489, Version: 06W.3)
    • Subject: Research Track (Methodological focus) (Compulsory subject)
      • 4.2'-4.3' Theoretical methodological courses I/II ( 0.0h VO/VK/VS/KU/PS / 6.0 ECTS)
        • 700.370 Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies (2.0h SE / 4.0 ECTS)

Equivalent courses for counting the examination attempts

Sommersemester 2024
  • 700.370 SE Seminar in LLM (large language models) application for Data Analytics in Intelligent Transportation (2.0h / 3.0ECTS)
Sommersemester 2022
  • 700.370 SE Seminar on Data Analytics in Intelligent Transportation (2.0h / 3.0ECTS)
Sommersemester 2021
  • 700.370 VS Seminar on "DATA ANALYTICS" and "PATTERN RECOGNITION" in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2020
  • 700.370 VS Seminar on "DATA ANALYTICS" and "PATTERN RECOGNITION" in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2019
  • 700.370 SE Seminar on Data Science in Intelligent Transportation (2.0h / 3.0ECTS)
Sommersemester 2017
  • 700.370 SE Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies (2.0h / 4.0ECTS)
Sommersemester 2016
  • 700.370 SE Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies (2.0h / 4.0ECTS)
Sommersemester 2014
  • 700.370 SE Seminar on Data mining and Pattern Recognition in Intelligent Vehicle Technologies (2.0h / 4.0ECTS)
Sommersemester 2013
  • 700.370 SE Seminar on Pattern Recognition in Intelligent Vehicle Technologies (2.0h / 4.0ECTS)