Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning) 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Ethem Alpaydin 作者

The MIT Press

译者

2010-02-26 出版日期

584 页数

USD 55.00 价格

Hardcover

丛书系列

9780262012430 图书编码

Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning) 在线电子书图书标签: 机器学习 MachineLearning 数据挖掘计算机科学 MIT CS AI 大数据

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发表于2024-12-23

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Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning) 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

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Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning) 在线电子书用户评价

评分☆☆☆☆☆

真的只是入门

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比Tom Mitchell那本好多了。內容很新組織也很清????。排得也不錯

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理论推导十分详尽

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比Tom Mitchell那本好多了。內容很新組織也很清????。排得也不錯

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比Tom Mitchell那本好多了。內容很新組織也很清????。排得也不錯 @2011-12-25 10:31:48

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The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.

Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning) 在线电子书下载 mobi epub pdf txt 在线电子书下载

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Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning) 在线电子书读后感

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为了对机器学习能有系统性的知识，买了这本书。因为书里各种公式占据了百分之七八十的比例，所以呵呵了。但是剩余的百分之三十可以读一读的，特别是需要对机器学习有个系统体系性的认识的话。这本书就一般吧。缺点就是数学公式太多了。

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