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 (obligatory to receive participaation certificate)
General Information University: University of Duisburg-Essen
• Learning Platform: Moodle (Link: https://lehre.moodle.uni-due.de/course/view.php?id=1986)
• Hybrid Format: Yes, the course includes both in-person and online sessions via Zoom
• Study Program and Level: Master’s/PhD students
• Weekly Hours: The event consists of three block sessions (lecture with tutorial sessions) with two additional dates for tutorials:
• First session: 02.04. (9:00–14:00)
• Following sessions: 03.04. & 04.04. (9:00–17:00)
• Online session: 07.04. (starts at 10:00; end time not specified)
• Final lecture: 25.04. (9:00–14:00)
• Language: German or English (depending on student preference)
Further information, including locations, and Zoom links, can be found on our homepage: Statistical Learning SS 25
