An Introduction To Pattern Recognition And Machine Learning

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Pattern Recognition and Machine Learning

Author : Christopher M. Bishop
Publisher : Springer
ISBN 13 : 9781493938438
Total Pages : 0 pages
Book Rating : 4.9/5 (384 download)

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Book Synopsis Pattern Recognition and Machine Learning by : Christopher M. Bishop

Download or read book Pattern Recognition and Machine Learning written by Christopher M. Bishop and published by Springer. This book was released on 2016-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Fundamentals of Pattern Recognition and Machine Learning

Author : Ulisses Braga-Neto
Publisher : Springer Nature
ISBN 13 : 3030276562
Total Pages : 357 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Fundamentals of Pattern Recognition and Machine Learning by : Ulisses Braga-Neto

Download or read book Fundamentals of Pattern Recognition and Machine Learning written by Ulisses Braga-Neto and published by Springer Nature. This book was released on 2020-09-10 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University. The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification. The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website.

Neural Networks for Pattern Recognition

Author : Christopher M. Bishop
Publisher : Oxford University Press
ISBN 13 : 0198538642
Total Pages : 501 pages
Book Rating : 4.1/5 (985 download)

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Book Synopsis Neural Networks for Pattern Recognition by : Christopher M. Bishop

Download or read book Neural Networks for Pattern Recognition written by Christopher M. Bishop and published by Oxford University Press. This book was released on 1995-11-23 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.

Pattern Recognition and Machine Learning

Author : Y. Anzai
Publisher : Elsevier
ISBN 13 : 0080513638
Total Pages : 424 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis Pattern Recognition and Machine Learning by : Y. Anzai

Download or read book Pattern Recognition and Machine Learning written by Y. Anzai and published by Elsevier. This book was released on 2012-12-02 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

Introduction to Pattern Recognition

Author : Sergios Theodoridis
Publisher : Academic Press
ISBN 13 : 0080922759
Total Pages : 233 pages
Book Rating : 4.0/5 (89 download)

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Book Synopsis Introduction to Pattern Recognition by : Sergios Theodoridis

Download or read book Introduction to Pattern Recognition written by Sergios Theodoridis and published by Academic Press. This book was released on 2010-03-03 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision. - Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth Edition - Solved examples in Matlab, including real-life data sets in imaging and audio recognition - Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)

Pattern Recognition

Author : Sergios Theodoridis
Publisher : Elsevier
ISBN 13 : 008051362X
Total Pages : 705 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis Pattern Recognition by : Sergios Theodoridis

Download or read book Pattern Recognition written by Sergios Theodoridis and published by Elsevier. This book was released on 2003-05-15 with total page 705 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to "learn" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the authors through use in their own classrooms.*Approaches pattern recognition from the designer's point of view*New edition highlights latest developments in this growing field, including independent components and support vector machines, not available elsewhere*Supplemented by computer examples selected from applications of interest

Information Theory, Inference and Learning Algorithms

Author : David J. C. MacKay
Publisher : Cambridge University Press
ISBN 13 : 9780521642989
Total Pages : 694 pages
Book Rating : 4.6/5 (429 download)

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Book Synopsis Information Theory, Inference and Learning Algorithms by : David J. C. MacKay

Download or read book Information Theory, Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

Introduction To Pattern Recognition And Machine Learning

Author : M Narasimha Murty
Publisher : World Scientific
ISBN 13 : 9814656275
Total Pages : 402 pages
Book Rating : 4.8/5 (146 download)

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Book Synopsis Introduction To Pattern Recognition And Machine Learning by : M Narasimha Murty

Download or read book Introduction To Pattern Recognition And Machine Learning written by M Narasimha Murty and published by World Scientific. This book was released on 2015-04-22 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics — neural networks, support vector machines and decision trees — attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the subject matter.

Pattern Recognition and Classification

Author : Geoff Dougherty
Publisher : Springer Science & Business Media
ISBN 13 : 1461453232
Total Pages : 203 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Pattern Recognition and Classification by : Geoff Dougherty

Download or read book Pattern Recognition and Classification written by Geoff Dougherty and published by Springer Science & Business Media. This book was released on 2012-10-28 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.

Mathematics for Machine Learning

Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
ISBN 13 : 1108569323
Total Pages : 392 pages
Book Rating : 4.1/5 (85 download)

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Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

A First Course in Machine Learning

Author : Simon Rogers
Publisher : CRC Press
ISBN 13 : 1498738540
Total Pages : 428 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis A First Course in Machine Learning by : Simon Rogers

Download or read book A First Course in Machine Learning written by Simon Rogers and published by CRC Press. This book was released on 2016-10-14 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/"

Pattern Recognition and Neural Networks

Author : Brian D. Ripley
Publisher : Cambridge University Press
ISBN 13 : 9780521717700
Total Pages : 420 pages
Book Rating : 4.7/5 (177 download)

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Book Synopsis Pattern Recognition and Neural Networks by : Brian D. Ripley

Download or read book Pattern Recognition and Neural Networks written by Brian D. Ripley and published by Cambridge University Press. This book was released on 2007 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.

Real and Functional Analysis

Author : Serge Lang
Publisher : Springer Science & Business Media
ISBN 13 : 1461208971
Total Pages : 591 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Real and Functional Analysis by : Serge Lang

Download or read book Real and Functional Analysis written by Serge Lang and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is meant as a text for a first-year graduate course in analysis. In a sense, it covers the same topics as elementary calculus but treats them in a manner suitable for people who will be using it in further mathematical investigations. The organization avoids long chains of logical interdependence, so that chapters are mostly independent. This allows a course to omit material from some chapters without compromising the exposition of material from later chapters.

A Concise Introduction to Machine Learning

Author : A.C. Faul
Publisher : CRC Press
ISBN 13 : 1351204742
Total Pages : 335 pages
Book Rating : 4.3/5 (512 download)

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Book Synopsis A Concise Introduction to Machine Learning by : A.C. Faul

Download or read book A Concise Introduction to Machine Learning written by A.C. Faul and published by CRC Press. This book was released on 2019-08-01 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise. This useful reference should be an essential on the bookshelves of anyone employing machine learning techniques. The author's webpage for the book can be accessed here.

Patterns, Predictions, and Actions: Foundations of Machine Learning

Author : Moritz Hardt
Publisher : Princeton University Press
ISBN 13 : 0691233721
Total Pages : 321 pages
Book Rating : 4.6/5 (912 download)

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Book Synopsis Patterns, Predictions, and Actions: Foundations of Machine Learning by : Moritz Hardt

Download or read book Patterns, Predictions, and Actions: Foundations of Machine Learning written by Moritz Hardt and published by Princeton University Press. This book was released on 2022-08-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers

Introduction to Statistical Pattern Recognition

Author : Keinosuke Fukunaga
Publisher : Elsevier
ISBN 13 : 0080478654
Total Pages : 606 pages
Book Rating : 4.0/5 (84 download)

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Book Synopsis Introduction to Statistical Pattern Recognition by : Keinosuke Fukunaga

Download or read book Introduction to Statistical Pattern Recognition written by Keinosuke Fukunaga and published by Elsevier. This book was released on 2013-10-22 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.

Introduction to Machine Learning

Author : Ethem Alpaydin
Publisher : MIT Press
ISBN 13 : 0262028182
Total Pages : 639 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Introduction to Machine Learning by : Ethem Alpaydin

Download or read book Introduction to Machine Learning written by Ethem Alpaydin and published by MIT Press. This book was released on 2014-08-22 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.