Authors: Dr. D. David Neels Ponkumar, Dr. R. Julian Menezes, Dr. Parthiban Aravamudhan, Mrs. Pavithra Karthik, Dr. V. Diana Earshia

ISBN: 978-81-997985-9-5

DOI: https://doi.org/10.59646/573

Date of Publication: January 26, 2026

Preface

Artificial Intelligence and Machine Learning have become central pillars of modern computing, shaping how intelligent systems perceive information, reason under uncertainty, learn from data, and support complex decision-making across virtually every domain of human activity. Fundamentals of Artificial Intelligence and Machine Learning is designed as a comprehensive and structured introduction to these fields, offering a balanced integration of classical AI foundations and modern machine learning methodologies. The book begins with the core principles of Artificial Intelligence, introducing intelligent agents, rationality, environments, problem-solving, search strategies, planning, game playing, and knowledge representation, thereby establishing the conceptual backbone of intelligent behavior. Classical techniques such as uninformed and heuristic search, adversarial reasoning, constraint satisfaction problems, logical inference, probabilistic reasoning, and expert systems are presented with clarity to help readers understand how intelligence can be formally modeled and implemented. Equal emphasis is placed on reasoning under uncertainty through Bayesian inference, probabilistic networks, and decision-theoretic approaches, reflecting real-world conditions where incomplete and noisy information is the norm. The second part of the book transitions smoothly into Machine Learning, grounding readers in the mathematical foundations of learning, including linear algebra, probability theory, hypothesis spaces, inductive bias, PAC learning, and the bias–variance trade-off. It then explores supervised, unsupervised, ensemble, and neural network–based learning paradigms, supported by detailed discussions on model evaluation, experimental design, and performance measurement. By progressively building from fundamental theory to practical algorithms and applications, the book enables readers to develop both conceptual understanding and analytical rigor. Intended for undergraduate and postgraduate students, educators, and aspiring AI practitioners, this text serves as a foundational resource that prepares readers for advanced study while equipping them with the critical thinking skills needed to design, evaluate, and responsibly deploy intelligent and learning-based systems in real-world environments.