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.
Feature extraction, basics of probability theory (conditional probabilities, marginal distributions, independence, covariance matrices, etc.), Bayes theorem, simple classifiers (kNN, nearest neighbor, persceptron, etc.)
