Intelligent Information Systems: AI Foundations for Decision Making

Businesses and organizations rely on sophisticated systems to process vast amounts of data and make critical decisions quickly. Understanding how to integrate artificial intelligence into information systems is essential for modern technical roles. This course provides a comprehensive introduction to the principles governing intelligent information systems. By the end, you will understand the core components of AI-driven systems and be able to structure data flows for automated analysis and intelligent decision support. What you'll learn: Understand the core architectures and components of modern intelligent information systems (IIS). Apply foundational machine learning concepts, including supervised and unsupervised methods, for data analysis. Learn techniques for effective data preparation and feature engineering necessary for training robust AI models. Practice basic prompt engineering strategies for interacting with and integrating generative AI components. Analyze the ethical considerations and requirements for explainability (XAI) in automated decision-making systems. Configure rule-based systems and knowledge representation methods for expert system design. We begin with key terminology and the historical context of AI, then move through data handling, model training fundamentals, and integration patterns. The course concludes by focusing on practical applications in data analysis and decision support. This course is designed for absolute beginners interested in the intersection of data science, computer science, and artificial intelligence, requiring no prior experience in programming or advanced mathematics. Start reading today and build your expertise in the future of automated decision systems.