AI Manager: Methods of Artificial Intelligence

Course overview

Modern AI applications are based on powerful learning methods, high-quality data, and suitable technical infrastructure. In addition to classical machine learning methods, neural networks, deep learning, and generative AI are becoming increasingly important. In this course, you will learn the key methods of data preparation, model training, and AI development, and gain an overview of the legal, ethical, and technical requirements for the use of modern AI systems. Content: Data preparation, data quality, and unsupervised learning with clustering and dimensionality reduction methods Basics of neural networks, deep learning, and transfer learning Development and training of neural networks using TensorFlow and Keras Hyperparameter optimization as well as evaluation and visualization of AI models Image recognition, text processing, and generative AI with modern AI architectures Prompt engineering and control of generative AI systems Applications of generative AI in text, image, speech, and video applications Data ethics, AI governance, and legal requirements under GDPR and EU AI regulation Integration of AI into business processes and use of modern AI and automation tools AI applications in the context of IoT, sensors, and real-time data processing Perspectives: The acquired knowledge provides a foundation for activities in the fields of artificial intelligence, machine learning, deep learning, data science, data engineering, business analytics, software development, digitalization, as well as in companies, research institutions, and consulting organizations that develop, implement, or operate AI-based applications. For questions regarding this and combination options, please feel free to contact our consulting.

Upcoming dates

• 2026-10-20T22:00:00.000Z · 2026-11-02T23:00:00.000Z · Altenburg, Thüringen · Blended Learning
• 2026-10-20T22:00:00.000Z · 2026-11-02T23:00:00.000Z · Amberg, Oberpfalz · Blended Learning
• 2026-10-20T22:00:00.000Z · 2026-11-02T23:00:00.000Z · Aachen · Blended Learning
• 2026-10-20T22:00:00.000Z · 2026-11-02T23:00:00.000Z · Aue, Sachsen · Blended Learning
• 2026-10-20T22:00:00.000Z · 2026-11-02T23:00:00.000Z · Aschaffenburg · Blended Learning