Pattern Recognition and Machine Learning (Hardcover)

Christopher M. Bishop

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商品描述

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.

商品描述(中文翻譯)

這是第一本以貝葉斯觀點呈現的模式識別教科書。該書介紹了近似推理算法,使得在無法得到精確答案的情況下能夠快速獲得近似答案。在其他書籍中很少應用圖形模型於機器學習,但本書使用圖形模型來描述機率分佈。不需要先備的模式識別或機器學習概念知識。需要熟悉多變量微積分和基礎線性代數,對概率的使用有一些經驗會有幫助,但不是必要的,因為本書包含了一個獨立的基礎概率理論介紹。