Probabilistic Machine Learning - An Introduction

Probabilistic approaches have become foundational in modern machine learning, offering a principled framework for modeling uncertainty, learning from data, and making robust predictions. Kevin P. Murphy’s Probabilistic Machine Learning – An Introduction provides readers with a comprehensive exploration of this paradigm, blending theoretical underpinnings with practical insights. The book guides readers through the fundamentals of probability theory, the construction of probabilistic models, and the execution of inference and learning algorithms, making it a vital resource for practitioners and students alike who wish to understand the power and flexibility of probabilistic modeling.

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