Machine Learning: Reinforcement Learning for Adaptive Systems

Course overview

Modern data pipelines optimize the processing of large data volumes for machine learning. In the course Machine Learning: Reinforcement Learning for Adaptive Systems, participants develop structured workflows for automating data harmonization and transformation to efficiently train reinforcement learning models. Data Preparation and Analysis SQL supports the powerful preparation of training data. Topics include optimized queries, joins, and aggregations that allow for the targeted structuring of large datasets and their preparation for adaptive machine learning systems. Exploratory data analysis serves to identify relevant states and reward functions. Statistical methods support data-driven decisions in model optimization and enhance the understanding of dynamic learning processes. Visualization Power BI facilitates the visualization of learning processes in reinforcement learning. Interactive dashboards and DAX functions enable a clear representation of learning trajectories and effective communication of data-driven optimization steps. Reinforcement Learning Reinforcement learning extends classical learning methods with the ability to operate in dynamic environments. The focus is on training autonomous models, conducting policy optimizations, and the targeted application of reinforcement learning in various fields.

Upcoming dates

• 2026-09-13T22:00:00.000Z · 2027-05-24T22:00:00.000Z · Berlin · Hybrid Learning
• 2026-09-13T22:00:00.000Z · 2027-05-24T22:00:00.000Z · München · Hybrid Learning
• 2026-09-13T22:00:00.000Z · 2027-05-24T22:00:00.000Z · Berlin · Hybrid Learning
• 2026-09-13T22:00:00.000Z · 2027-05-24T22:00:00.000Z · Frankfurt am Main · Hybrid Learning
• 2026-09-13T22:00:00.000Z · 2027-05-24T22:00:00.000Z · Stuttgart · Hybrid Learning