Detection and Pattern Recognition

Overview of the Detection and Pattern Recognition Lecture & Exercise


If you would like to participate in this course, please register via C@mpus.

Please register for both the lecture and the exercise. The lecture and exercise materials can be found in the ILIAS course "Detection and Pattern Recognition - Exercises". You should get access to the ILIAS course at the beginning of the lecture period, as long as you have also registered for the exercise in Campus.


Lecture email
Lecturer Prof. Dr.-Ing. Bin Yang
Assistant Mario Döbler

Contents of the Lecture

1. Introduction

  • Statistical signal processing
  • Detection
  • Pattern recognition
  • Confusion matrix

2. Bayesian Decision Theory

  • Bayesian theorem
  • Minimum Bayesian risk decision
  • Minimax decision
  • Neyman-Pearson decision
  • Bayesian parameter estimation

3. Detection

4. Supervised Classification Methods

  • Overview
  • Template matching
  • Bayes Plug-In Rule
  • Density estimation
  • Discriminant functions
  • Comparison of classifiers

5. Unsupervised learning

  • Overview
  • K-Means

6. Feature dimension reduction

  • Feature selection
  • Feature transform

7. Feature extraction

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