Pattern Recognition and
Image Processing Group
Institute of Visual Computing and Human-Centered Technology
Introduction to Pattern Recognition
Aim of course
This lecture teaches the basics in pattern recognition and gives an overview of the most important methods. Its focus lies on the analysis of images, i.e. extraction and processing of features, and classification of the extracted data. The corresponding exercise (186.840) deepens the understanding of the topics of the lecture.
Subject of course
Feature extraction, basics of probability theory (conditional probabilities, marginal distributions, independence, covariance matrices, etc.), Bayes theorem, simple classifiers (kNN, nearest neighbor, persceptron, etc.), ...