The course “Introduction to Artificial Intelligence” provides a foundational overview of the main concepts, methods, and paradigms of artificial intelligence (AI). It is designed to help students understand how intelligent systems are built, how they reason, and how they learn from data.

The course begins with a general introduction to AI, covering its definition, historical evolution, major application domains, and current challenges. It then introduces Symbolic Artificial Intelligence, which focuses on knowledge representation, logic, and rule-based reasoning, illustrating how explicit symbols and inference mechanisms are used to model intelligent behavior.

Next, the course explores Data-driven AI and Machine Learning, highlighting the shift from knowledge-based systems to learning-based approaches. A dedicated chapter on Supervised Learning presents core algorithms and concepts such as classification, regression. Finally, the course addresses Data Collection and Preparation, emphasizing the importance of data quality, preprocessing, handling missing values, and feature engineering as essential steps for building effective AI models.

By the end of the course, students will have a solid understanding of both symbolic and data-driven approaches to AI and the fundamental role of data in intelligent systems.