Foundations of Artificial Intelligence: Theory and Algorithms

Artificial Intelligence powers modern technology, but truly mastering it requires a deep understanding of the underlying mathematical and algorithmic principles. This course provides the conceptual framework you need to move beyond simple tool usage. By the end of this course, you will be able to clearly articulate the differences between core AI approaches, understand the mechanics of various learning models, and analyze how data and logic drive intelligent behavior in complex systems. What you'll learn: * Understand the core terminology, history, and major branches of Artificial Intelligence, Machine Learning, and Deep Learning. * Master the foundational principles of classic algorithms, including decision trees, clustering, and regression models. * Learn the theoretical basis of modern neural networks, including concepts like activation functions and basic transformer architecture. * Apply concepts of data preprocessing, feature engineering, and evaluating model performance using key metrics. * Understand the critical importance of model explainability, fairness, and ethical considerations in AI system design. We begin with fundamental definitions and progress methodically through classical machine learning paradigms before exploring the theoretical mechanics of modern deep learning and its implications. The content emphasizes reading comprehension and applying theoretical knowledge through written exercises. This course is designed for absolute beginners with no prior experience in AI or advanced mathematics. If you are curious about the "how" and "why" behind intelligent systems, this is the perfect starting point. Start building your foundational knowledge of AI theory today.