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.

Lectures

1. The concept of Artificial Intelligence, its research and application areas. The concept of the problem space and the possibilities of narrowing it down. 2. Illustrating problems with directed graphs: general and special pathfinding problems. 3. Graph representation: search task in graphs. -graphs, AND/OR graphs. 4. Search algorithms. Representation of two-player games, existence of winning strategy. Minimax algorithm and its corrections (e.g. alphabet truncation). 5. Evolutionary algorithms. Concepts of population, fitness function, criterion function. 6. Evolutionary strategies, genetic algorithms. Agents. Properties of agents (ideal rational, autonomous), structure and categories (reflexive, goal-oriented, utility-oriented). 7. Learning agents. The relationship between an agent and its environment. Multi-agent systems. Communication between agents: cooperating and competing agents. 8. Concepts of coordination and cooperation. Swarm intelligence, swarm intelligence in nature. 9. The concept of the robot, the milestones of its development. Robot hardware, sensors and actuators. 10. Sensing in robotics, positioning, mapping. Motion planning: configuration space, cell decomposition, skeletonization method. 11. Structure and classification of robots: manipulators, mobile robots, humanoids, androids. The living spaces and areas of use of robots, nano technology, dynamic grip recognition, brain fingerprinting, the relationship between the animal world and robotics. 12. Detection and navigation. Imaging, image processing tools, 3-D information extraction. 13. Navigation and manipulation using vision. Navigation and motion planning. Speech recognition. Fuzzy logic. Fuzzy inference (Mamdani, Takagi-Sugeno).

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 and study aids are available in Moodle.