Machine Learning Models and Algorithms for Big Data Classification, Shan Suthaharan

Автор: Kevin Murphy
Название: Machine Learning
ISBN: 0262018020 ISBN-13(EAN): 9780262018029
Издательство: MIT Press
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Цена: 124150.00 T
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Описание:

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Автор: Mohammed
Название: Machine Learning,Algorithms And App
ISBN: 1498705383 ISBN-13(EAN): 9781498705387
Издательство: Taylor&Francis
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Цена: 84710.00 T
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Описание: Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.

Автор: John J. Grefenstette
Название: Genetic Algorithms for Machine Learning
ISBN: 0792394070 ISBN-13(EAN): 9780792394075
Издательство: Springer
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Цена: 186290.00 T
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Описание: Features the articles that were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference.

Автор: Barber
Название: Bayesian Reasoning and Machine Learning
ISBN: 0521518148 ISBN-13(EAN): 9780521518147
Издательство: Cambridge Academ
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Цена: 73920.00 T
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Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.

Автор: Wittek Peter
Название: Quantum Machine Learning: What Quantum Computing Means to Data Mining
ISBN: 0128100400 ISBN-13(EAN): 9780128100400
Издательство: Elsevier Science
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Цена: 78590.00 T
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Описание: Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. . Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications.

Автор: Hurwitz, Kaufman Marcia, Bowles Adrian
Название: Cognitive Computing: Implementing Big Data Machine Learning Solutions
ISBN: 1118896629 ISBN-13(EAN): 9781118896624
Издательство: Wiley
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Цена: 40120.00 T
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Описание: A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data.

Автор: Panos Pardalos; Mario Pavone; Giovanni Maria Farin
Название: Machine Learning, Optimization, and Big Data
ISBN: 3319279254 ISBN-13(EAN): 9783319279251
Издательство: Springer
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Цена: 52170.00 T
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Описание: This bookconstitutes revised selected papers from the First International Workshop onMachine Learning, Optimization, and Big Data, MOD 2015, held in Taormina, Sicily,Italy, in July 2015. The 32papers presented in this volume were carefully reviewed and selected from 73submissions.

Автор: Buduma Nikhil
Название: Fundamentals of Deep Learning: Designing Next-Generation Artificial Intelligence Algorithms
ISBN: 1491925612 ISBN-13(EAN): 9781491925614
Издательство: Wiley
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Цена: 36950.00 T
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Описание: In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. If you`re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.

Автор: Mitchell
Название: Machine Learning
ISBN: 0071154671 ISBN-13(EAN): 9780071154673
Издательство: McGraw-Hill
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Цена: 69770.00 T
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Описание: Covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

Автор: Bekkerman
Название: Scaling up Machine Learning
ISBN: 0521192242 ISBN-13(EAN): 9780521192248
Издательство: Cambridge Academ
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Цена: 98210.00 T
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Описание: In many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a variety of machine learning methods developed specifically for parallel or distributed systems, covering algorithms, platforms and applications.

Автор: Clarke
Название: Principles and Theory for Data Mining and Machine Learning
ISBN: 0387981349 ISBN-13(EAN): 9780387981345
Издательство: Springer
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Цена: 186330.00 T
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Описание: Extensive treatment of the most up-to-date topicsProvides the theory and concepts behind popular and emerging methodsRange of topics drawn from Statistics, Computer Science, and Electrical Engineering

Автор: Marsland
Название: Machine Learning
ISBN: 1466583282 ISBN-13(EAN): 9781466583283
Издательство: Taylor&Francis
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Цена: 80630.00 T
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Описание:

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.

Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

New to the Second Edition

• Two new chapters on deep belief networks and Gaussian processes
• Reorganization of the chapters to make a more natural flow of content
• Revision of the support vector machine material, including a simple implementation for experiments
• New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
• Additional discussions of the Kalman and particle filters
• Improved code, including better use of naming conventions in Python

Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author's website.