Pattern Recognition and Machine Learning

Livro digital

Título:
Pattern Recognition and Machine Learning

Autor:
Christopher M. Bishop

Categoria:
Tecnologia > IA

Doador:
Raffaello D. N.

Sinopse:
Christopher Bishop’s Table of Contents makes the book’s structure clear from the start: it opens with probability theory and foundational concepts, then moves through linear models, kernel methods, graphical models, and sequential data before finishing with sampling, approximate inference, and a sequence of advanced applications. That progression gives the reader a map of how pattern recognition and machine learning fit together as one field rather than two separate subjects. The book is written by a single author, Christopher M. Bishop, and it is grounded in his perspective as both researcher and teacher. Beyond the title, the early chapters and preface reveal a deliberate balance between statistical principles and practical algorithms, with exercises throughout and a self-contained introduction to the probability tools needed to follow the material. The contents also show that it goes well past basics, covering Bayesian methods, graphical models, kernels, and approximate inference in a unified way. Its value lies in that combination of breadth and rigor: it is not just an introduction, but a structured path from core ideas to modern methods and real technique. Readers get a compact, mathematically serious guide to the main ideas that shaped contemporary machine learning, plus a reference that rewards both study and later consultation.