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A First Course in Machine Learning, Second Edition, Rogers



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Цена: 9437р.
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Автор: Rogers
Название:  A First Course in Machine Learning, Second Edition
ISBN: 9781498738484
Издательство: Taylor&Francis
Классификация:
ISBN-10: 1498738486
Обложка/Формат: Mixed media product
Страницы: 427
Вес: 0.794 кг.
Дата издания: 26.07.2016
Серия: Chapman & hall/crc machine learning & pattern recognition
Язык: English
Издание: 2 ed
Иллюстрации: 8 tables, black and white; 186 illustrations, black and white
Размер: 165 x 243 x 28
Читательская аудитория: Tertiary education (us: college)
Ключевые слова: Automatic control engineering, BUSINESS & ECONOMICS / Statistics,COMPUTERS / Databases / Data Mining,COMPUTERS / Machine Theory
Основная тема: Machine Learning
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Англии
Описание: The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models.



      Старое издание

Machine Learning

Автор: Kevin Murphy
Название: Machine Learning
ISBN: 0262018020 ISBN-13(EAN): 9780262018029
Издательство: MIT Press
Рейтинг:
Цена: 18622 р.
Наличие на складе: Ожидается поступление.

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.

Pattern Recognition and Machine Learning

Автор: Christopher M. Bishop
Название: Pattern Recognition and Machine Learning
ISBN: 0387310738 ISBN-13(EAN): 9780387310732
Издательство: Springer
Рейтинг:
Цена: 11878 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: 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.

Practical Machine Learning with H2O

Автор: Darren Cook
Название: Practical Machine Learning with H2O
ISBN: 149196460X ISBN-13(EAN): 9781491964606
Издательство: Wiley
Рейтинг:
Цена: 6334 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.

Bayesian Reasoning and Machine Learning

Автор: Barber
Название: Bayesian Reasoning and Machine Learning
ISBN: 0521518148 ISBN-13(EAN): 9780521518147
Издательство: Cambridge Academ
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Цена: 10611 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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.

Scaling up Machine Learning

Автор: Bekkerman
Название: Scaling up Machine Learning
ISBN: 0521192242 ISBN-13(EAN): 9780521192248
Издательство: Cambridge Academ
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Цена: 14096 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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.

Machine Learning

Автор: Mitchell
Название: Machine Learning
ISBN: 0071154671 ISBN-13(EAN): 9780071154673
Издательство: McGraw-Hill
Рейтинг:
Цена: 10466 р.
Наличие на складе: Поставка под заказ.

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.

Machine Learning

Автор: Marsland
Название: Machine Learning
ISBN: 1466583282 ISBN-13(EAN): 9781466583283
Издательство: Taylor&Francis
Рейтинг:
Цена: 10889 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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.


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