AI

Course overview

AI includes the core modules AI Level 1 and English. Fundamentals of Modern Data Processing Systems The training course imparts the fundamentals of modern data processing systems as well as the procedural application of data-driven strategies through the targeted use of AI in corporate practice. The focus is on understanding basic system architectures, as the use of AI sustainably optimizes administrative processes, target group analyses, and operational processes. By working through technical principles, participants learn how AI-based systems contribute to structuring operational workflows and information flows. The seminar highlights the functioning of language and data processing systems to properly manage the use of AI in automating analytical methods and decision models. A key focus is on the precise formulation of technical instructions, for which solid English skills provide a significant advantage when using AI, as essential system documentation, developer tools, and global language models are predominantly available in English. Furthermore, the course addresses the recognition of logical errors and hallucinations that may occur when applying AI in system research or data evaluations. Connecting external data sources with AI models is also an important element for the reliable provision of company-relevant information in the corporate environment. Another aspect concerns data protection regulations under the EU AI Act and ethical guidelines to ensure the use of AI is in line with corporate compliance standards. Finally, the quality control of automated system outputs is addressed to anchor the use of AI in the professional work environment in a compliant and results-oriented manner. Graduates acquire solid expertise to accompany data-driven work processes using modern AI systems methodically, efficiently, and with international compatibility. Theoretical and Technical Foundations The course conveys the theoretical and technical foundations of Machine Learning and Deep Learning in the first focus area. Participants learn to distinguish basic learning forms such as Supervised Learning, Unsupervised Learning, and Reinforcement Learning for analyzing complex data structures within the framework of Data Mining. A central focus is on the functioning of generative systems, Natural Language Processing, Large Language Models, as well as the underlying Transformer architecture and the Attention Mechanism. Participants acquire solid skills in Prompt Engineering using Zero-Shot and Few-Shot prompting for the targeted generation of texts, requirement specifications, and structured data. Additionally, the course covers advanced methods such as Retrieval-Augmented Generation for integrating internal company knowledge bases and techniques for fine-tuning pre-trained models. Although these technical contents are taught in German, there is a significant advantage in practice from solid English skills, as the majority of global literature, code libraries,…

Upcoming dates

• 2026-10-18T22:00:00.000Z · 2027-01-05T23:00:00.000Z · Nürnberg, Mittelfranken · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-05T23:00:00.000Z · Wiesbaden · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-05T23:00:00.000Z · München · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-05T23:00:00.000Z · Stuttgart · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-05T23:00:00.000Z · München · Combined Learning