Pattern Recognition and Machine Learning
As an Amazon Associate and Bookshop.org affiliate, BookMatcher earns from qualifying purchases.
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Listed under Computers · Intelligence (AI) & Semantics · Computer Graphics · Computer Vision & Pattern Recognition
About Pattern Recognition and Machine Learning
What is Pattern Recognition and Machine Learning about?
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning.
Who wrote Pattern Recognition and Machine Learning?
Pattern Recognition and Machine Learning was written by Christopher M. Bishop.
What books are similar to Pattern Recognition and Machine Learning?
Explore our curated recommendations for books like Pattern Recognition and Machine Learning by Christopher M. Bishop, matched by theme, writing style, and atmosphere to help you find your next read.
Where can I buy Pattern Recognition and Machine Learning?
You can buy Pattern Recognition and Machine Learning in print or ebook through the Amazon and Bookshop.org links on this page — Bookshop.org purchases also support independent bookstores.