Mixed Models = (*| generalized| non-) linear Mixed-effects Models, extend traditional regression models by adding "random effect" terms.In applications, such models are called "hierarchical models", "repeated measures" or "split plot designs". Mixed models are widely used and appropriate in an aera of complex data measured from living creatures from biology to human sciences.

Support vector machines and kernel methods for classification;EM algorithm;Unsupervised learning and clustering algorithms

We discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations. The class is hands-on and methods are applied using the statistical programming language R.

Block course only on prediction problems, aka "supervised learning".Part 1, Classification: logistic regression, linear/quadratic discriminant analysis, Bayes classifier; additive and tree models; further flexible ("nonparametric") methods.Part 2, Flexible Prediction: additive models, MARS, Y-Transformation models (ACE,AVAS); Projection Pursuit Regression (PPR), neural nets.

This course focusses on nonparametric estimation of probability densities and regression functions. These recent methods allow modelling without restrictive assumptions such as 'linear function'. These smoothing methods require a weight function and a smoothing parameter. Focus is on one dimension, higher dimensions and samples of curves are treated briefly. Exercises at the computer.

Deeper understanding of R: Function calls, rather than "commands".Reproducible research and data analysis via Sweave and Rmarkdown.Limits of floating point arithmetic.Understanding how functions work. Environments, packages, namespaces.Closures, i.e., Functions returning functions.Lists and [mc]lapply() for easy parallelization.Performance measurement and improvements.

Regression studies the dependence of a random response variable on other variables. We consider the theory and application of linear regression with one or more covariates. Various extensions such as nonlinear models, generalized linear models, nonparametric models, robust methods, and model selection are discussed.

"Statistics Lab" is an Applied Statistics Workshop in Data Analysis. It provides a learning environment in a realistic setting.Students lead a regular consulting session at the Seminar für Statistik (SfS). After the session, the statistical data analysis is carried out and a written report and results are presented to the client. The project is also presented in the course's seminar.

The course provides the first part an introduction to the statistical/graphical/data science software R (https://www.r-project.org/) for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects.

The course provides the second part an introduction to the statistical software R for scientists. Topics are data generation and selection, graphical functions, important statistical functions, types of objects, models, programming and writing functions.Note: This part builds on "Using R... (Part I)", but can be taken independently if the basics of R are already known.