Authors: Dr. B. Selvapriya, Dr. Mercy Bai. G, Dr. P. Venkadesh, and Dr. S.V. Divya

ISBN: 978-81-996233-3-0

DOI: https://doi.org/10.59646/548

Date of Publication: December 24, 2025

About the Book:

The book Artificial Intelligence and Machine Learning is designed as a comprehensive, conceptually rigorous, and application-oriented exposition of one of the most transformative intellectual and technological movements of the modern era. Artificial Intelligence (AI) and Machine Learning (ML) have fundamentally altered the way problems are formulated, knowledge is extracted from data, and decisions are made in complex, uncertain, and dynamic environments. This text is conceived with the clear objective of providing students, researchers, and practitioners with a deep and structured understanding of both the theoretical foundations and the practical mechanisms that drive intelligent systems. Beginning with the fundamentals of problem solving, the book situates AI within its historical, philosophical, and scientific context, tracing its evolution from early symbolic reasoning and search-based methods to contemporary data-driven and learning-based paradigms. The opening unit establishes a strong conceptual base by examining core AI capabilities, ethical and governance considerations, and the role of intelligent agents, before progressing through classical and heuristic search strategies, local and adversarial search, and constraint satisfaction problems—topics that reveal how intelligence can be modeled as systematic problem solving. The subsequent unit on probabilistic reasoning addresses one of the most critical challenges in AI: acting rationally under uncertainty. Through a detailed treatment of Bayesian inference, probabilistic models, decision theory, Markov decision processes, and causal reasoning, the book demonstrates how uncertainty can be quantified, reasoned about, and exploited for optimal decision-making. Building upon this foundation, the supervised learning unit offers an in-depth exploration of machine learning models, ranging from linear regression and classification to support vector machines, decision trees, and random forests, with careful attention to their mathematical structure, geometric interpretation, optimization principles, and generalization behavior. Ensemble techniques and unsupervised learning are then presented as powerful paradigms for improving predictive performance and discovering hidden structure in data, covering bagging, boosting, stacking, clustering, instance-based learning, Gaussian mixture models, and the expectation–maximization algorithm. The final unit is devoted to neural networks and deep learning, tracing the journey from the perceptron to multilayer architectures and deep networks, and examining activation functions, backpropagation, gradient-based optimization, vanishing gradients, regularization, batch normalization, and dropout with conceptual clarity and analytical depth. Throughout the book, emphasis is placed on unifying principles—such as optimization, probabilistic reasoning, bias–variance trade-offs, and representation learning—so that readers not only learn individual algorithms, but also develop a coherent mental framework for understanding intelligent systems as a whole. By integrating mathematical rigor, intuitive explanations, worked examples, and real-world applications, this book aspires to serve as both a foundational academic text and a practical guide, equipping readers with the intellectual tools necessary to engage critically and creatively with the rapidly evolving landscape of Artificial Intelligence and Machine Learning.