This laboratory course introduces students to a broad set of AI techniques used to support human decision-making in complex domains and to design autonomous or semi-autonomous systems. The course follows a project-oriented, capstone-style structure, in which students progressively design, implement, and evaluate an applied AI system addressing a real or realistic problem. The main component of the course is dedicated to the development of software prototypes using the AI methods introduced during the lectures. Students will work on hands-on challenges involving techniques such as supervised machine learning, natural language processing, information extraction, search and optimization methods, automated planning, constraint solving, and heuristic-based reasoning. The aim of this active participation is to give students a deeper understanding of the strengths, limitations, and practical issues that arise when applying AI methods to real-world tasks. Throughout the course, students will build a toolbox of computational methods and methodological skills enabling them to analyze, model, and solve a variety of practical problems. The hands-on approach will complement theoretical concepts with the practical know-how required to design and deploy concrete, functioning AI solutions.