Pattern Recognition and Machine Learning offers a unified introduction to two interrelated fields that have evolved from engineering and computer science. This text emphasizes the Bayesian approach, presenting graphical models and a variety of approximate inference algorithms that enable efficient, near-optimal solutions in complex scenarios. It also introduces kernel-based methods that have significantly impacted both theoretical developments and practical applications, creating a comprehensive framework for understanding probabilistic models. Designed for advanced undergraduates, first-year PhD students, researchers, and practitioners alike, this volume does not assume any prior expertise in pattern recognition or machine learning. A solid grasp of multivariate calculus and basic linear algebra is expected, while a self-contained introduction to probability theory supports readers from diverse backgrounds. With its detailed exploration of modern techniques, Pattern Recognition and Machine Learning stands as a vital resource in the contemporary study of these dynamic fields.
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