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/
Read MoreSeminar -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.
Read MoreIntroduction to R
Course description
R is a powerful programming language and statistical environment that is free to use and offers extensive data analysis and graphical visualization capabilities. This course takes participants from the initial introduction to R to the basics needed to further develop their skills independently or in subsequent R courses. In terms of content, the course covers the basics of programming in R. The course consists of short lecture sections, live coding, and many hands-on exercises to directly apply your new learning.
Read MoreHPC Series: Python on HPC Clusters
Short description
For May of 2025 we recommend the HPC Series: Python on HPC Clusters course, offered by the Jan Steiner and Shayma Wahdan at University of Bonn.
The course will teach you some of the Python-related topics beyond the absolute basics of the language - things you will likely run into as a scientist using Python.
The course is spread over two half-days. It will be held as a hybrid course - both online via Zoom and in person at HRZ (Wegelerstr. 6), room 0.012. The course language is English
Read MoreSeminar - 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).
Read MoreAdvanced R for Econometricians
Short description
For April of 2025 we recommend the Advance R for Econometrics Seminar, offered by the University of Duisburg-Essen.
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 concepts in programming (object orientation, profiling, debugging), packages for modern applications in data science and cutting-edge R extensions, e.g., for parallel computing and C++ integration.