Machine and Reinforcement Learning

Course overview

The course provides a detailed overview of the field of Machine Learning - here, artificial knowledge is generated from experience. Initially, you will learn the fundamentals, followed by the two categories of supervised and unsupervised learning, as well as the topic of evaluation and improvement. Subsequently, Reinforcement Learning, one of the three main techniques of machine learning, is described, where software is trained through direct interaction with its environment in the form of trial and error to achieve optimal results. Machine Learning Introduction to Machine Learning Supervised Learning Unsupervised Learning Evaluation and Improvement 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

• 2026-08-02T22:00:00.000Z · 2026-09-24T22:00:00.000Z · Karlsruhe, Baden · Combined Learning
• 2026-08-02T22:00:00.000Z · 2026-09-24T22:00:00.000Z · Alzenau in Unterfranken · Combined Learning
• 2026-08-02T22:00:00.000Z · 2026-09-24T22:00:00.000Z · Augsburg, Bayern · Combined Learning
• 2026-08-02T22:00:00.000Z · 2026-09-24T22:00:00.000Z · Bad Hersfeld · Combined Learning
• 2026-08-02T22:00:00.000Z · 2026-09-24T22:00:00.000Z · Bad Kreuznach · Combined Learning