AI Literacy: Our range of workshops
Machine learning package: Basic knowledge in three sessions
You don't know anything about programming, but still want to understand how AI models work? Machine learning (ML) is the basis of search engines, recommendation systems, translation services, image and speech recognition and much more. Here you have the opportunity to gain insights into machine learning and acquire basic knowledge in three workshops.
Can't attend all the dates? No problem - we will provide you with documents to help you prepare for the workshop.
1. fit for the future: AI terms, methods and typical application scenarios explained in an understandable way
Tuesday, 25.11.2025/ 10.00 - 13.00/ Presence
Learn to classify basic machine learning methods and recognize typical use cases - also in the context of your future professional field. You will learn about the limitations and challenges of data-based approaches so that you can assess their potential realistically. You will develop an understanding that will enable you to discuss ideas for AI applications with developers in a technically sound manner.
Registerhere.
2. how does AI learn? How are models evaluated? And what is overfitting?
Monday, 08.12.2025/ 13.45 - 16.45/ Presence
In this workshop, you will learn the basic principles of machine learning, from training to testing, without any programming. You will create your own regulations for real example data and recognize why seemingly perfect solutions often fail on unknown cases. You will also learn how to classify the quality of a model and recognize the causes of prediction errors.
Prerequisite: In preparation, we recommend taking the workshop "AI for non-developers: Classifying terms and methods, recognizing use cases" or review the documents provided in advance so that you can get started in the best possible way.
Registerhere.
3. from data to decisions: Classification in action
Tuesday, 06.01.2025/ 10.00 - 13.00/ Presence
Building on your basic ML knowledge, you will learn how classification models structure data, derive decisions and evaluate them using the Confusion Matrix. You will then build your own decision trees in AI Studio, investigate overfitting and find out which models generalize best, without any programming knowledge.
Prerequisite: In preparation, we recommend attending the workshops "AI for non-developers: Classifying terms and methods, recognizing use cases" and "How does AI learn? How are models evaluated? And what is overfitting?"or look through the documents provided in advance so that you can get started in the best possible way.
Registerhere.