Foundational AI: Reasoning Under Uncertainty

In the real world, data is rarely perfect or complete. Artificial intelligence systems must navigate this inherent uncertainty to be effective and trustworthy. This course introduces you to the essential principles that enable AI to reason and make sound judgments even when information is fuzzy or missing. By the end of this course, you will possess a clear understanding of the core mechanisms AI uses to handle imperfect knowledge, empowering you to critically evaluate and design more robust intelligent systems. What you'll learn: * Understand the fundamental role of probability and statistics in AI. * Apply principles of Bayesian inference for reasoning with uncertain data. * Explore various models for representing and manipulating uncertain knowledge, including probabilistic graphical models. * Analyze how AI systems interpret and make decisions from incomplete or noisy information. * Grasp the foundational concepts behind modern uncertainty quantification in machine learning. * Practice evaluating the confidence and limitations of AI-driven predictions. This course begins with core terminology and basic concepts, then progresses to explore various methods and models for managing uncertainty in AI. You will read through clear explanations and practical examples, building your knowledge step by step. This course is designed for absolute beginners with no prior experience in artificial intelligence or advanced mathematics required. If you are curious about how AI handles the complexities of the real world, this course is for you. Start your journey into the foundations of intelligent reasoning today.