Authors: Mr. M. Selvam, Ms. S. Sreelakshmy, Ms. M. Keerthana, Mrs. P. Usha

ISBN: 978-81-69297-43-1

DOI: https://doi.org/10.59646/637

Date of Publication: March 24, 2026

Cite this book: M. Selvam, S. Sreelakshmy, M. Keerthana, P. Usha, (2026), Essentials of Artificial Intelligence, San International Scientific Publications, ISBN: 978-81-69297-43-1, doi: https://doi.org/10.59646/637

Preface

The Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the modern era, reshaping industries, enhancing decision-making, and enabling machines to perform tasks that once required human intelligence. Essentials of Artificial Intelligence is designed as a comprehensive introductory text that provides readers with a clear and structured understanding of the fundamental concepts, methodologies, and applications of AI. The book begins with an overview of AI, exploring its definition, scope, historical evolution, and its distinction from related fields such as Machine Learning and Data Science, while also examining the characteristics, types, and real-world applications of intelligent systems along with their limitations. It then progresses to the study of intelligent agents and problem-solving techniques, covering agent architectures, rational decision-making, search strategies, heuristic methods, and constraint satisfaction problems.

Building upon this foundation, the book introduces knowledge representation and reasoning, including logical systems, inference mechanisms, semantic models, ontologies, and reasoning under uncertainty, which are essential for developing intelligent systems. The subsequent units focus on machine learning, beginning with fundamental paradigms, data handling, evaluation metrics, and model validation techniques, followed by a detailed exploration of supervised learning algorithms such as regression, classification methods, decision trees, support vector machines, and ensemble approaches. The text further delves into unsupervised learning and data mining techniques, including clustering, dimensionality reduction, anomaly detection, and pattern discovery, highlighting their significance in extracting insights from complex datasets. Finally, the book introduces the basics of neural networks and deep learning, explaining their biological inspiration, core architectures, learning algorithms, and applications in areas such as computer vision and speech recognition. Carefully structured and written in a clear, accessible manner, this book aims to equip students and beginners with both theoretical understanding and practical insights, enabling them to build a strong foundation in Artificial Intelligence and prepare for advanced study and real-world applications in this rapidly evolving field.