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Pattern Recognition and Machine Learning
Pattern Recognition and Machine Learning
Pattern Recognition and Machine Learning
Pattern Recognition and Machine Learning
Pattern Recognition and Machine Learning
Pattern Recognition and Machine Learning
Pattern Recognition and Machine Learning
Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

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Pattern Recognition and Machine Learning book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

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📚📚 I. THÔNG TIN SẢN PHẨM
📒 Mã sản phẩm :  STT946
📒 Nhà xuất bản : Springer (August 17, 2006)
📒 Tác giả :  Christopher M. Bishop 
📒 Ngôn ngữ :   Tiếng Anh
📒 ISBN      : 0387310738
📒 Số trang :  738  trang
📒 Hình thức : Bìa Mềm, IN ĐEN TRẮNG
📒 Loại : Sách gia công đóng gáy keo chắc chắn chất lượng cao
📒 Giấy in : Giấy ngoại định lượng 70msg, viết vẽ và hightlight thoải mái.
📒 Chất lượng : Bản in rõ nét, giá rất tốt cho mọi người.


📚📚 II. MÔ TẢ SẢN PHẨM
📒 1.Mô tả sản phẩm

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.

📒 2. Tác giả

​Chris Bishop is a Microsoft Distinguished Scientist and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, and in 2007 he was elected Fellow of the Royal Society of Edinburgh.

Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory. He then joined Culham Laboratory where he worked on the theory of magnetically confined plasmas as part of the European controlled fusion programme.

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