AI FOUNDATIONS : FROM HUMAN INTELLIGENCE TO MACHINE LEARNING BY Dr.Sapna Jain, Mr.Ragesh T S, Dr.Reshma M, Mohammad Ashique Azad
Indo-continental Academic Publishers · Zenodo (CERN European Organization for Nuclear Research) · 2025
Artificial Intelligence (AI) has emerged as one of the most transformative and interdisciplinary fields of the twenty-first century, reshaping the way humans interact with machines and redefining problem-solving across science, engineering, business, healthcare, and society at large. From early philosophical inquiries into human intelligence to modern data-driven machine learning models, AI represents a continuous journey toward understanding and replicating intelligent behavior. The book AI Foundations: From Human Intelligence to Machine Learning has been collaboratively authored by a team of academicians and researchers with diverse expertise in computer science, data science, cognitive science, and engineering. This multi-author approach brings together complementary perspectives, ensuring a balanced presentation that integrates theoretical foundations, computational models, and practical applications of artificial intelligence. The text begins by exploring the concept of human intelligence, cognitive processes, and the historical evolution of artificial intelligence. It then systematically introduces core AI principles, including problem-solving, knowledge representation, search techniques, and reasoning. Building upon these foundations, the book delves into machine learning paradigms such as supervised, unsupervised, and reinforcement learning, along with neural networks and deep learning concepts. Emphasis is placed on understanding both the mathematical underpinnings and the intuitive insights behind modern AI algorithms. Special attention has been given to clarity of explanation, logical progression of topics, and real-world relevance. Each chapter is structured to support effective learning through clear definitions, illustrative examples, diagrams, and application-oriented discussions. Ethical considerations, societal impact, and future directions of AI are also addressed, encouraging readers to engage critically with both the opportunities and challenges posed by intelligent systems. This book is intended for undergraduate and postgraduate students of computer science, artificial intelligence, data science, and allied disciplines. It also serves as a valuable reference for educators, researchers, and professionals seeking a structured understanding of AI fundamentals and machine learning techniques. The content has been aligned with contemporary university curricula and emerging industry requirements. The authors sincerely hope that this collaborative effort will help readers develop a strong conceptual foundation in artificial intelligence, inspire innovation, and foster responsible development of intelligent technologies. Constructive feedback from readers and instructors is warmly welcomed and will contribute to the continuous improvement of future editions of this book.