Time and place:
Information regarding the online teaching will be provided via studon.
- Thu 10:15-11:45, Room H4
- Fri 10:15-11:45, Room H4
Fields of study
- WPF ME-BA-MG6 from SEM 3
- WPF MT-MA-BDV from SEM 1
- PF IuK-MA-MMS-INF from SEM 1
- PF ICT-MA-MPS from SEM 1
- WPF CE-MA-INF from SEM 1
- WPF INF-MA from SEM 1
- WPF CME-MA from SEM 1
- WF ASC-MA from SEM 1
- WPF ME-MA-MG6 from SEM 1
This lecture gives an introduction into the basic and commonly used
classification concepts. First the necessary statistical concepts are
revised and the Bayes classifier is introduced. Further concepts include generative and discriminative models such as the Gaussian classifier and Naive Bayes, and logistic regression, Linear Discriminant Analysis, the Perceptron and Support Vector Machines (SVMs). Finally more complex methods like the Expectation Maximization Algorithm, which is used to estimate the parameters of Gaussian Mixture Models (GMM), are discussed.
In addition to the mentioned classifiers, methods necessary for
practical application like dimensionality reduction, optimization
methods and the use of kernel functions are explained.
Finally, we focus on Independent Component Analysis (ICA), combine weak classifiers to get a strong one (AdaBoost), and discuss the performance of machine classifiers.
In the tutorials the methods and procedures that are presented in this lecture are illustrated using theoretical and practical exercises.
- lecture notes - Duda R., Hart P. and Stork D.: Pattern Classification - Niemann H.: Klassifikation von Mustern - Niemann H.: Pattern Analysis and Understanding
Keywords: Mustererkennung, maschinelle Klassifikation
Expected participants: 150