Foundations of AI Models and Algorithms

Artificial intelligence is the engine driving modern technology, but understanding how it truly works requires a grasp of its underlying logic. This course cuts through the hype to provide a solid, foundational understanding of the core models and algorithms that drive intelligent systems. By the end of this course, you will have a deep conceptual grasp of the primary families of AI algorithms, including search, optimization, machine learning paradigms, and basic neural network structures. You will be prepared to read technical literature and explore specialized AI fields with confidence. What you'll learn: * Learn the historical context and foundational definitions of artificial intelligence and intelligent agents. * Understand classical search algorithms (e.g., A* and greedy search) and constraint satisfaction problems. * Master the core concepts of supervised, unsupervised, and reinforcement learning paradigms. * Practice analyzing the mathematical foundations of fundamental models like decision trees and linear regression. * Configure basic neural network architectures and understand the principles of backpropagation. * Apply introductory concepts in prompt engineering to interact effectively with large language models. * Build a framework for assessing AI system performance, bias, and basic ethical considerations. The material begins with core terminology and agent theory, progresses through classical AI techniques, and culminates in an exploration of modern machine learning and deep learning fundamentals. Written explanations are paired with practical exercises to solidify your understanding of theoretical concepts. This course is designed for absolute beginners with no prior experience in artificial intelligence, machine learning, or advanced mathematics. Start building your essential knowledge base in AI algorithms today.