The course provides a practical and conceptual foundation in Artificial Intelligence, with a particular emphasis on Generative Artificial Intelligence, Large Language Models (LLMs), and their responsible application in engineering. Upon completing the course, students will be able to: - Understand the principles, capabilities, and limitations of Artificial Intelligence, Generative AI, and Large Language Models. - Design, test, and refine prompts to obtain accurate and useful AI-generated outputs. - Critically evaluate AI-generated content, identify hallucinations, biases, and misinformation, and validate responses using reliable sources. - Apply ethical principles and understand the implications of the EU AI Act and responsible AI usage. - Perform practical exercises using AI tools and real-world engineering scenarios. - Execute basic API interactions with Large Language Models using predefined examples. - Generate and evaluate text and images using multimodal AI systems while considering correctness and ethical aspects. - Apply AI techniques to practical engineering problems through a mini-project relevant to the student's field of study. - Understand the basic concepts of embeddings, vector search, and AI-assisted information retrieval.

Labs

1. Introduction to Artificial Intelligence, Generative AI, and Large Language Models – overview, key concepts, capabilities, and practical examples 2. Large Language Models – principles of text generation, strengths, limitations, and common applications 3. Prompt Engineering – prompt design, testing, refinement, and context management 4. Hallucinations, Bias, and AI Evaluation – identifying limitations, validating outputs, and critical thinking 5. AI Agents and Workflow Automation – introduction to AI agents, tool calling, and workflow concepts 6. Midterm Written Test 1 7. Responsible AI – ethics, privacy, intellectual property, and the EU AI Act 8. Practical Exercises with AI APIs, Prompting, and Engineering Applications 9. Image Generation and Multimodal AI – prompt design, image generation, output evaluation, and ethical considerations 10. Mini-project – practical application of AI techniques to an engineering-related problem 11. Embeddings, Vector Search, and AI-assisted Information Retrieval 12. Midterm Written Test 2 13. Optional Make-up / Remedial Week (Retake Test)

Lecture slides, practical exercises, and supplementary study materials are provided by the instructor.