Comprehensive introduction to pattern recognition and machine learning based on probabilistic methods and statistical modelling. Covers supervised and unsupervised learning, probability theory, Bayesian inference, graphical models, neural networks, kernel methods, support vector machines, mixture models, latent variable models, and approximate inference techniques. Emphasizes mathematical foundations and practical algorithms for data analysis, prediction, classification, clustering, and intelligent decision-making systems. Widely used as a graduate-level textbook and reference in machine learning, artificial intelligence, data science, and pattern recognition.