Прикладной ИИ для промышленной и управленческой инженерии

Modern industrial and management engineering relies heavily on data to drive efficiency and innovation. Understanding how to leverage artificial intelligence is no longer optional for engineers and managers looking to optimize complex systems. This written course equips you with the foundational knowledge to integrate AI and machine learning techniques into industrial workflows. You will transition from understanding basic data principles to conceptualizing smart systems that predict failures, streamline logistics, and enhance decision-making. What you'll learn: Understand foundational AI concepts, machine learning algorithms, and their specific utility in industrial settings; Analyze operational data using modern data processing frameworks to identify bottlenecks; Apply predictive maintenance strategies to reduce equipment downtime and optimize asset lifecycles; Optimize supply chain logistics and inventory management using intelligent forecasting models; Explore basic MLOps concepts to understand how AI models are deployed and monitored in production; Design data-driven management workflows that improve project scheduling and resource allocation. The course begins with essential terminology and the core concepts of decision-making before moving into practical industrial applications. You will explore real-world scenarios and walk through written implementation examples that show how AI solves engineering challenges. This course is designed for aspiring industrial engineers, operations managers, and students who want to bridge the gap between engineering and data science. No prior background in artificial intelligence or advanced programming is required. Start reading today to build the skills needed to lead intelligent industrial operations.