Probabilistic Machine Learning - An Introduction

Livro digital

Título:
Probabilistic Machine Learning - An Introduction

Autor:
Kevin P. Murphy

Categoria:
Tecnologia > IA

Doador:
Raffaello D. N.

Sinopse:
Probabilistic machine learning is the kind of book readers reach for when they need more than heuristics, but less hand-waving than a typical survey. Its table of contents opens with a clean run from introduction to probability, statistics, decision theory, information theory, linear algebra, and optimization, which makes the book’s architecture feel as deliberate as its subject. Kevin P. Murphy builds the material from first principles into a full toolkit: univariate and multivariate probability, Bayes’ rule, Gaussian models, estimation, regularization, Bayesian and frequentist decision-making, then the mathematical machinery needed to use these ideas well. Later chapters move through linear models, deep neural networks, nonparametric methods, and unsupervised topics such as dimensionality reduction, clustering, recommender systems, and graph embeddings. The result is a serious introduction that does not stay introductory for long. Readers get a broad, methodical path from uncertainty and inference to modern machine learning practice, with enough depth to support real study and implementation. It is especially valuable for anyone who wants a rigorous map of the field rather than a quick tour of popular algorithms.