Pattern Recognition and Machine Learning Information Science and Statistics Christopher M Bishop 9780387310732 Books Lecteur PDF gratuit MQI

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Lire en ligne Pattern Recognition and Machine Learning Information Science and Statistics Christopher M Bishop 9780387310732 Books HFX

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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.

Christopher M. Bishop,Pattern Recognition and Machine Learning (Information Science and Statistics),Springer,0387310738,Intelligence (AI) Semantics,Computer science,Machine learning,Machine learning.,Optical data processing,Pattern perception,Pattern perception.,Pattern recognition systems.,Statistics,Artificial intelligence,B,COMPUTER,COMPUTERS / Computer Graphics,COMPUTERS / Computer Vision Pattern Recognition,COMPUTERS / Intelligence (AI) Semantics,Computer Applications,Computer Books General,Computer Science,Computer Vision Pattern Recognition,Computer vision,Computer/General,Computers,Computers - General Information,Computers/Artificial Intelligence - Computer Vision Pattern Recognition,Image Processing and Computer Vision,Image processing,Image processing graphics (static images),Informatik, EDV / Anwendungs-Software,Intelligence (AI) Semantics,MATHEMATICS / Probability Statistics / General,Machine learning,Machine learning.,Mathematics for scientists engineers,Non-Fiction,Optical data processing,Pattern Recognition,Pattern perception,Pattern perception.,Pattern recognition systems,Pattern recognition systems.,Probability statistics,Scholarly/Graduate,Statistics,Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences,Textbooks (Various Levels),United States,algorithms; bioinformatics; classification; computer vision; data mining; learning; machine learning; statistics,algorithms;bioinformatics;classification;computer vision;data mining;learning;machine learning;statistics,COMPUTERS / Computer Graphics,COMPUTERS / Computer Vision Pattern Recognition,COMPUTERS / Intelligence (AI) Semantics,Computer Vision Pattern Recognition,Computers/Artificial Intelligence - Computer Vision Pattern Recognition,MATHEMATICS / Probability Statistics / General,Computers - General Information,Pattern recognition systems,Computers,Computer Books General,Artificial intelligence,Computer vision,Image processing,Probability statistics,Image processing graphics (static images),Mathematics for scientists engineers,Pattern recognition,Informatik, EDV / Anwendungs-Software

Pattern Recognition and Machine Learning Information Science and Statistics Christopher M Bishop 9780387310732 Books Reviews :

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.

Christopher M. Bishop,Pattern Recognition and Machine Learning (Information Science and Statistics),Springer,0387310738,Intelligence (AI) Semantics,Computer science,Machine learning,Machine learning.,Optical data processing,Pattern perception,Pattern perception.,Pattern recognition systems.,Statistics,Artificial intelligence,B,COMPUTER,COMPUTERS / Computer Graphics,COMPUTERS / Computer Vision Pattern Recognition,COMPUTERS / Intelligence (AI) Semantics,Computer Applications,Computer Books General,Computer Science,Computer Vision Pattern Recognition,Computer vision,Computer/General,Computers,Computers - General Information,Computers/Artificial Intelligence - Computer Vision Pattern Recognition,Image Processing and Computer Vision,Image processing,Image processing graphics (static images),Informatik, EDV / Anwendungs-Software,Intelligence (AI) Semantics,MATHEMATICS / Probability Statistics / General,Machine learning,Machine learning.,Mathematics for scientists engineers,Non-Fiction,Optical data processing,Pattern Recognition,Pattern perception,Pattern perception.,Pattern recognition systems,Pattern recognition systems.,Probability statistics,Scholarly/Graduate,Statistics,Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences,Textbooks (Various Levels),United States,algorithms; bioinformatics; classification; computer vision; data mining; learning; machine learning; statistics,algorithms;bioinformatics;classification;computer vision;data mining;learning;machine learning;statistics,COMPUTERS / Computer Graphics,COMPUTERS / Computer Vision Pattern Recognition,COMPUTERS / Intelligence (AI) Semantics,Computer Vision Pattern Recognition,Computers/Artificial Intelligence - Computer Vision Pattern Recognition,MATHEMATICS / Probability Statistics / General,Computers - General Information,Pattern recognition systems,Computers,Computer Books General,Artificial intelligence,Computer vision,Image processing,Probability statistics,Image processing graphics (static images),Mathematics for scientists engineers,Pattern recognition,Informatik, EDV / Anwendungs-Software

Pattern Recognition and Machine Learning (Information Science and Statistics) [Christopher M. Bishop] on . PThis 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

 

Product details Series Information Science and Statistics Hardcover 738 pages Publisher Springer (April 6, 2011) Language English ISBN-10 0387310738

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