Seminar

By DKZ.2R: Seminar - Statistical and Machine Learning Methods

Course Description and Goals:

Statistical learning is a field that teaches students how to analyze and interpret data by applying statistical methods and machine learning algorithms to uncover patterns, make predictions, and gain insights from data. The syllabus includes: • Statistical and machine learning methods, including linear and polynomial regression, logistic regression, and linear discriminant analysis. • Model validation techniques such as cross-validation and bootstrap, model selection, and regularization methods (ridge and lasso). • Nonlinear models, splines, and generalized additive models. • Tree-based methods, including random forests and boosting. • Support-vector machines and an introduction to causal inference. • Unsupervised learning methods such as principal components analysis and clustering (k-means and hierarchical). Examination Format: Report and Presentation. Further information, including locations, and Zoom links, can be found on our homepage: https://oek.wiwi.uni-due.de/studium-lehre/lehrveranstaltungen/sommersemester-26/statistical-learning-vorlesung-17350/

By DKZ.2R: Seminar -Advanced R

Course Description and Goals:

The course teaches advanced topics in R programming that become increasingly relevant for everyday applications in both applied and theoretical econometrics and empirical economics. It covers, amongst other topics:

• Advanced programming concepts, including object orientation, profiling, and debugging. • Packages for modern applications in data science. • Cutting-edge R extensions, for example for parallel computing and C++ integration. • Applications relevant to empirical economics and econometrics.

By DKZ.2R: Seminar - Statistical and Machine Learning Methods

Course Description and Goals:

Statistical learning is a field that teaches students how to analyze and interpret data by applying statistical methods and machine learning algorithms to uncover patterns, make predictions, and gain insights from data. The syllabus includes:

• Statistical and machine learning methods, including linear and polynomial regression, logistic regression, and linear discriminant analysis.

• Model validation techniques such as cross-validation and bootstrap, model selection, and regularization methods (ridge and lasso).