Machine Learning Theory, Algorithms, and Applications

Machine Learning is at the core of modern artificial intelligence, driving innovation across industry, finance, transportation, and cybersecurity. This book offers a comprehensive and rigorous treatment of machine learning , combining solid mathematical foundations with real-world applications and hands-on implementations.
Covering supervised, semi-supervised, unsupervised, deep, and reinforcement learning , the book explains both classical and modern algorithms, including linear and logistic regression, decision trees, random forests, support vector machines, Bayesian models, neural networks, CNNs, LSTMs, GANs, and reinforcement learning methods based on Markov decision processes.
With hundreds of pages of in-depth explanations , practical case studies, and problem sets with full solutions , readers will learn how to design, analyze, and deploy machine learning models using Python and MATLAB . Advanced topics such as statistical learning theory, bias-variance trade-off, regularization, optimization, probabilistic inference, and hyperparameter tuning are treated in detail.
The book also presents unique industrial and economic applications , including fraud detection, manufacturing optimization, computer vision, natural language processing, cyber-attack detection, and the development of virtual sensors for autonomous vehicles , addressing the challenges of the green economy.
Designed for graduate students, researchers, engineers, and professionals , this book serves both as a textbook and a long-term reference for mastering machine learning theory and practice.

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