Machine and Reinforcement Learning with Statistics

Course overview

The course connects the topics of statistics for verifying observations and categorizing measurement data, as well as machine learning - where artificial knowledge is generated from experience. In the first part, statistical methods for comparing two or more groups are taught, along with the use of a statistical program and an introduction to experimental design. In the second part, the application areas, categories, and concepts of machine learning are presented, before introducing reinforcement learning, one of the three main techniques of machine learning, where software is trained through direct interaction with its environment in the form of trial and error to achieve optimal results. Statistics and Data Analysis Statistical fundamentals Artificial Intelligence (AI) in the work process Methods for comparing two groups Basics of regression analysis Methods for comparing means of multiple groups Introduction to experimental design (DoE, Design of Experiments) Project work in statistics Machine Learning Introduction to machine learning and ML workflow: Modern AI and machine learning methods, machine learning workflow, scikit-learn Data preparation and feature engineering: Data quality, data preprocessing, scaling, encoding, feature selection Supervised learning - regression and classification: Ensemble methods, boosting methods, evaluation metrics, feature importance, explainable AI Unsupervised learning: Clustering methods, anomaly detection Time series analysis and forecasting: Basics, analysis, forecasting methods Neural networks and modern AI: Basics of deep learning, natural language processing, foundation models, large language models, generative AI Deployment of ML models: Storage and deployment, development of simple ML applications, interfaces and platforms Project work in machine learning Reinforcement Learning Machine learning: Reinforcement learning Markov Decision Processes (MDPs) Q-Learning Deep Reinforcement Learning Advanced topics Practical applications Summary and outlook Project work in reinforcement learning Changes are possible; the course content is regularly updated.

Upcoming dates

• 2026-11-15T23:00:00.000Z · 2027-02-11T23:00:00.000Z · Aachen · Combined Learning
• 2026-11-15T23:00:00.000Z · 2027-02-11T23:00:00.000Z · Augsburg, Bayern · Combined Learning
• 2026-11-15T23:00:00.000Z · 2027-02-11T23:00:00.000Z · Berlin · Combined Learning
• 2026-11-15T23:00:00.000Z · 2027-02-11T23:00:00.000Z · Berlin · Combined Learning
• 2026-11-15T23:00:00.000Z · 2027-02-11T23:00:00.000Z · Bochum · Combined Learning

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