This course is also available in German language.
What does the workshop offer?
Artificial Intelligence and Machine Learning open up new possibilities for software, business processes, and digital products. At the same time, they raise technical, economic, legal, and ethical questions that need to be considered when evaluating AI projects.
This English-language workshop explains the foundations of modern Artificial Intelligence and shows how AI systems work, how they are used, and how they can be assessed critically. The focus is on Machine Learning, Large Language Models, neural networks, and common AI workflows.
What will participants learn?
- Understand key terms and concepts in Artificial Intelligence and Machine Learning
- Differentiate between Supervised, Unsupervised, and Reinforcement Learning
- Understand Generative and Predictive AI as well as Large Language Models
- Understand training, validation, testing, finetuning, and transfer learning
- Assess uncertainty, bias, explainability, and other weaknesses of AI models
- Understand the roles of RAG, embeddings, vector databases, and prompting in AI applications
- Consider economic, legal, and ethical aspects of Artificial Intelligence
Who is the course suitable for?
The workshop is designed especially for managers, project leaders, and software architects who want to evaluate the use of AI components and better understand their opportunities and risks.
No specific technical prerequisites are required apart from a general interest in information technology. The seminar is conducted in English.
What will participants receive?
- Two days of online training from 9:00 a.m. to 5:00 p.m.
- Interactive sessions and practical exercises on key AI topics
- An introduction to the practical use of OpenAI models
- Vendor-independent and largely technology-independent foundational knowledge of Machine Learning and Artificial Intelligence
- Training in a group of 4 to 12 participants with Dr. Michael Weiss
Why this workshop?
The workshop combines technical foundations with concrete case studies and practical exercises. In addition to ChatGPT and other Large Language Models, it also covers applications such as image recognition, image generation, and transcription.
Participants learn not only about possible applications but also about the limitations and risks of modern AI models. This provides a solid foundation for evaluating Artificial Intelligence projects in a professional context.
Agenda
What is Artificial Intelligence?
- Examples of Artificial Intelligence that illustrate the breadth of applications
- Supervised, Unsupervised, and Reinforcement Learning
- Generative vs. Predictive AI
- Embeddings and Vector Databases, e.g., for AI-based search applications
- Case Study: How DeepBlue defeated the chess world champion
- Practical Task: Recognize application areas in your own industry
Introduction to Large Language Models (LLM)
- How can ChatGPT etc. generate text?
- System and User Prompts
- What are the fundamental weaknesses of this approach?
- Randomness and Creativity in LLMs
- Use of non-public and current data with Retrieval Augmented Generation (RAG)
Multi-Modal LLMs
- Practical Task: Prompting OpenAI’s LLMs
From Linear Regression to Artificial Neural Networks
- How machines learn from data
- What is an Artificial Neural Network?
- Application examples of Artificial Neural Networks
- Strengths of Artificial Neural Networks and Artificial Intelligence
The Machine Learning Workflow
- Training, Validation, and Testing of Models
- Finetuning existing models
- Transfer Learning from existing models
- Practical Task: Hallucinations of large language models
Weaknesses of Artificial Neural Networks
- Uncertainties in predictions
- Explainability of predictions
- Problems with unknown data
- Bias
- Practical Task: Live-Jailbreaking (“Hacking”) Google’s image generator*
* Execution and success cannot be guaranteed.
Economic Aspects of Artificial Intelligence
- Costs in Machine Learning
- Self-improving systems and AI economies of scale
- Legal and Ethical Questions without obvious solutions
- Case Study: Tesla’s self-driving cars
Brief overview of known services
- Text and Multi-modal Models (GPT variants, LLaMA…)
- Image generation models (DALL·E, Stable Diffusion, Imagen…)
- Transcription models (Whisper)
- Simple and inexpensive alternatives (FastText, Pre-trained Image Recognition…)
