Machine Learning

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: Introduction to Machine Learning with Python and Scikit-Learn

As part of our “Trainings” work package, the DKZ.2R creates, curates and presents a variety of free trainings, seminars and courses. Our next offering will be a one and a half day carpentries-style workshop on basic machine learning using python and scikit-learn, to be presented at RWTH Aachen University on 1st and 2nd of April, 2026. The workshop will cover the following topics:

• What is Machine Learning / Why bother?
• Supervised Methods (Regression / Classification)
• Ensemble Methods
• Unsupervised Methods (Clustering / Dimensionality Reduction)
• Neural Networks
• Ethics and Implications of Machine Learning

Workshop material is available online and will be presented by instructors who will walk you through the steps and are available for questions throughout the event. The official registration is already closed, for last-minute registrations please contact us via info@dkz2r.de.

By DKZ.2R: Data Analytics for Engineering Data Using Machine Learning

We are happy to announce our new course “Data analytics for engineering data using machine learning”. The event will take place online on three consecutive days in february of 2026. This three-day online workshop addresses the preparation, analysis and interpretation of numerical simulation data by machine learning methods. Besides the introduction of the most important concepts like clustering, dimensionality reduction, visualization and prediction, this course provides several practical hands-on tutorials using the python libraries numpy, scikit-learn and pytorch as well as the SCAI DataViewer.

By DKZ.2R: Introduction to Machine Learning with Python and Scikit-Learn

As part of our “Trainings” work package, the DKZ.2R creates, curates and presents a variety of free trainings, seminars and courses. Our next offering will be a one and a half day carpentries-style workshop on basic machine learning using python and scikit-learn, to be presented at RWTH Aachen University on 24th and 25th of July, 2025. The workshop will cover the following topics:

• What is Machine Learning / Why bother?
• Supervised Methods (Regression / Classification)
• Ensemble Methods
• Unsupervised Methods (Clustering / Dimensionality Reduction)
• Neural Networks
• Ethics and Implications of Machine Learning

Workshop material is available online and will be presented by instructors who will walk you through the steps and are available for questions throughout the event. If you are interested in taking part in the workshop, you can sign up here.

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).