Introduction: Decision Making and Artificial Intelligence Course In today's rapidly evolving business landscape, technology has brought about a revolution, transforming the way companies operate. This shift encompasses not only the technological aspects but also has a profound impact on the decision-making processes within organizations and their markets. As businesses face the challenges posed by data explosion and the need for real-time decision support, advanced automated solutions have become essential for managing increasingly complex problems. Consequently, decision-making algorithms, which encompass the rules, predictions, constraints, and logic guiding decision-making, are also evolving to meet these challenges. The primary objective of the "Decision Making and Artificial Intelligence" course is to enhance students' skills in designing effective decision-making processes, with a specific focus on utilizing quantitative methods to develop and implement decision-making solutions. Throughout the course, students will gain a comprehensive understanding of the principles and techniques involved in designing and delivering decision-making solutions. The course begins by introducing students to the fundamentals of identifying key elements of a solution using measurable and repeatable methodologies. This involves identifying decision-makers, users, crucial resources and technologies, objectives, and constraints. Students will learn how to effectively communicate these elements with stakeholders and subsequently formalize them through a structured model. Notably, the course emphasizes the differences in solution design when addressing various types of decisions, including strategic, tactical, operational, and real-time scenarios. In the second part of the course, the focus shifts to quantitative-based methods. Students will delve into the essentials of combinatorial optimization, exact methods, heuristics, metaheuristic methods, and Artificial Intelligence. Additionally, the course will introduce students to the practical usage of commercial solvers. By the end of the course, students will have gained a comprehensive skill set enabling them to effectively design and implement decision-making processes using quantitative methods. They will be equipped to tackle complex problems and leverage Artificial Intelligence techniques to deliver optimal solutions. Through practical exercises and real-world case studies, students will develop the necessary proficiency to navigate the evolving landscape of decision-making in the digital era.