{"product_id":"artificial-intelligence-machine-learning-workshop","title":"AI Artificial Intelligence – Foundations, Opportunities, Risks: 2-Day Virtual Seminar (English)","description":"\u003cp\u003e\u003cstrong\u003eThis course is also available in \u003ca href=\"https:\/\/karrierewelt.golem.de\/products\/ki-kuenstliche-intelligenz-grundlagen-chancen-risiken-virtueller-zwei-tage-workshop\"\u003eGerman language\u003c\/a\u003e.\u003c\/strong\u003e\u003c\/p\u003e\n\u003ch2\u003eWhat does the workshop offer?\u003c\/h2\u003e\n\u003cp\u003eArtificial 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.\u003c\/p\u003e\n\u003cp\u003eThis 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.\u003c\/p\u003e\n\n\u003ch2\u003eWhat will participants learn?\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eUnderstand key terms and concepts in Artificial Intelligence and Machine Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDifferentiate between Supervised, Unsupervised, and Reinforcement Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eUnderstand Generative and Predictive AI as well as Large Language Models\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eUnderstand training, validation, testing, finetuning, and transfer learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAssess uncertainty, bias, explainability, and other weaknesses of AI models\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eUnderstand the roles of RAG, embeddings, vector databases, and prompting in AI applications\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eConsider economic, legal, and ethical aspects of Artificial Intelligence\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eWho is the course suitable for?\u003c\/h2\u003e\n\u003cp\u003eThe 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.\u003c\/p\u003e\n\u003cp\u003eNo specific technical prerequisites are required apart from a general interest in information technology. The seminar is conducted in English.\u003c\/p\u003e\n\n\u003ch2\u003eWhat will participants receive?\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eTwo days of online training from 9:00 a.m. to 5:00 p.m.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eInteractive sessions and practical exercises on key AI topics\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAn introduction to the practical use of OpenAI models\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eVendor-independent and largely technology-independent foundational knowledge of Machine Learning and Artificial Intelligence\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eTraining in a group of 4 to 12 participants with Dr. Michael Weiss\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eWhy this workshop?\u003c\/h2\u003e\n\u003cp\u003eThe 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.\u003c\/p\u003e\n\u003cp\u003eParticipants 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.\u003c\/p\u003e\n\n\u003ch2\u003eAgenda\u003c\/h2\u003e\n\n\u003ch3\u003eWhat is Artificial Intelligence?\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eExamples of Artificial Intelligence that illustrate the breadth of applications\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eSupervised, Unsupervised, and Reinforcement Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eGenerative vs. Predictive AI\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eEmbeddings and Vector Databases, e.g., for AI-based search applications\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCase Study: How DeepBlue defeated the chess world champion\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePractical Task: Recognize application areas in your own industry\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eIntroduction to Large Language Models (LLM)\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eHow can ChatGPT etc. generate text?\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eSystem and User Prompts\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eWhat are the fundamental weaknesses of this approach?\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eRandomness and Creativity in LLMs\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eUse of non-public and current data with Retrieval Augmented Generation (RAG)\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eMulti-Modal LLMs\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003ePractical Task: Prompting OpenAI’s LLMs\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eFrom Linear Regression to Artificial Neural Networks\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eHow machines learn from data\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eWhat is an Artificial Neural Network?\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eApplication examples of Artificial Neural Networks\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eStrengths of Artificial Neural Networks and Artificial Intelligence\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eThe Machine Learning Workflow\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eTraining, Validation, and Testing of Models\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eFinetuning existing models\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eTransfer Learning from existing models\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePractical Task: Hallucinations of large language models\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eWeaknesses of Artificial Neural Networks\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eUncertainties in predictions\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eExplainability of predictions\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eProblems with unknown data\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eBias\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePractical Task: Live-Jailbreaking (“Hacking”) Google’s image generator*\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e* Execution and success cannot be guaranteed.\u003c\/p\u003e\n\n\u003ch3\u003eEconomic Aspects of Artificial Intelligence\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eCosts in Machine Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eSelf-improving systems and AI economies of scale\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLegal and Ethical Questions without obvious solutions\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCase Study: Tesla’s self-driving cars\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eBrief overview of known services\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eText and Multi-modal Models (GPT variants, LLaMA…)\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eImage generation models (DALL·E, Stable Diffusion, Imagen…)\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eTranscription models (Whisper)\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eSimple and inexpensive alternatives (FastText, Pre-trained Image Recognition…)\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Dr. Michael Weiss","offers":[{"title":"8.–9. September 2025 \/ 9:00–17:00","offer_id":54095765897484,"sku":"AkaKI4mgr-EN","price":1500.0,"currency_code":"EUR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0588\/4333\/2813\/products\/ki-kunstliche-intelligenz-fur-manager-chancen-risiken-virtueller-zwei-tage-workshop-859472.jpg?v=1772634808","url":"https:\/\/karrierewelt.golem.de\/products\/artificial-intelligence-machine-learning-workshop","provider":"Golem Karrierewelt","version":"1.0","type":"link"}