Pattern Recognition and Machine Learning

Description

Pattern Recognition and Machine Learning presents a systematic introduction to machine learning through the lens of probability theory and statistical modeling. Rather than focusing solely on algorithms, the book explains how uncertainty, inference, and probabilistic reasoning form the foundation of pattern recognition. It examines topics such as Bayesian networks, graphical models, kernel methods, mixture models, approximate inference, and neural networks, making it an essential reference for readers seeking a rigorous understanding of modern machine learning techniques.