Artificial Intelligence for Users
This introductory course on Machine Learning and Deep Learning provides you with an overview, context, and understanding of how these techniques actually work. You’ll learn which problems are suitable for ML and Deep Learning, which techniques are associated with them, and why simplicity is often better than complexity. No programming required—just clear concepts and practical examples. By the end of the course, you’ll be able to come up with realistic AI applications for your own field.
Machine Learning and Deep Learning form the foundation of many modern AI applications. They determine how systems recognize patterns, make predictions, and support decisions based on data. At the same time, the theory behind machine learning remains unclear and fragmented for many professionals. This course provides an overview, context, and understanding of how machine learning and deep learning actually work and when they are—and are not—useful.
Machine learning isn’t about magic, but about building statistical models that learn from examples. In this course, you’ll learn how data is converted into models, how those models generalize to new situations, and what assumptions play a role in that process. No programming required—just clear concepts, intuitive explanations, and relatable examples. This will help you get a handle on a subject that’s often presented as unnecessarily complex.
Deep learning is a specific form of machine learning that is primarily used with complex data such as text, images, and sound. Many contemporary AI applications, including language models and image recognition, are based on this. In this course, you’ll learn how deep learning relates to classical machine learning and why simplicity is often more effective than complexity.
The Introduction to Machine Learning course is not a technical training program, but a fundamental introduction to the thinking behind AI systems. You’ll gain a better understanding of what happens “under the hood,” enabling you to realistically evaluate and apply AI applications.
Dive into the world of machine learning and discover how data is transformed into predictive models. You won’t learn how to program models, but how to recognize machine learning as a potential solution to real-world problems. From predictions and classification to segmentation and pattern recognition: the applications are broad and span multiple sectors.
What makes machine learning unique is that systems are not explicitly programmed, but learn from examples. This offers many opportunities, but also entails clear limitations and risks. In this course, you will develop a realistic and critical perspective on AI and machine learning, free from hype and buzzwords.
For professionals, this means a different way of thinking about data and decision-making. Machine learning can support analyses, predictions, and scenarios, but requires the right expectations. You will learn when ML adds value and when other solutions are better.
Machine learning is not a distant dream. By taking this course, you will lay a solid foundation for better contributing to discussions about AI applications within your organization or field.
Machine learning is based on a number of fundamental principles. Understanding these principles is essential for evaluating applications and devising your own use cases:
Data and Examples
Machine learning learns from data. The quality, quantity, and representativeness of the data largely determine the result.
Models and Generalization
A model learns patterns from data and applies them to new situations. Good generalization is more important than perfect performance on training data.
Recognizing Problem Types
Not every problem is suitable for machine learning. Recognizing the right type of problem is crucial for success.
Key Concepts You’ll Learn:
By understanding these basic principles, you’ll develop a clear mental model of how machine learning works.
In this course, you’ll combine insight with practical application. You’ll develop the following skills, among others:
Specific topics:
Practical applications
The course is strongly focused on recognition and application. Examples of applications include:
You’ll learn to use machine learning as a conceptual framework, not as a black box.
Choosing this course means you’ll learn how machine learning and deep learning really work, so you can better contribute ideas, make decisions, and apply these concepts in a world where AI is becoming increasingly central.
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Contact usParticipants will gain a solid mental model of machine learning and deep learning. They will understand what happens under the hood, the types of problems ML solves, and where its limitations lie.
Morning: Fundamentals & Context
Participants will be able to explain in their own words what ML is and why it isn’t magic.
Afternoon: Types of ML problems & models
Practical exercises:
Participants will be able to identify which types of problems are suitable for machine learning and which are not.
Participants will learn to apply machine learning as a conceptual framework. They will be able to devise, evaluate, and discuss realistic use cases within their own field.
Morning: Algorithms Without the Technical Details
Participants understand why certain techniques are suitable for certain problems.
Afternoon: Use cases, choices & pitfalls
Practical exercises:
The participant:
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
No. The course focuses on understanding and providing an overview, not on code or formulas. You’ll learn how machine learning works and how to apply it, without any prior technical knowledge.
After completing the course, you will be able to identify which problems are suitable for machine learning and how AI applications can add value within your field. You will be better equipped to contribute to discussions, evaluate options, and support your decisions.
You’ll learn how models are developed conceptually, but not how to program them. The focus is on understanding what happens “under the hood” so that you can realistically assess applications.
Yes. Especially if you use AI tools, this course will help you better understand what they can and cannot do. This makes it easier to use them more effectively and to maintain a critical perspective on the results.
