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机器学习

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Jingdong book

机器学习

Name: 机器学习
Brand: Jingdong book
SKU: 3118186071
Price: 38.21 USD
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《机器学习》是迄今市面上内容全面的机器学习教材之一，书中汇集了所有用于理解、挖掘和分析数据的先进方法，并且通过数百个精选实例和解说性插图，直观而准确地阐释了这些方法背后的原理，内容涵盖了机器学习的构成要素和机器学习任务、逻辑模型、几何模型、统计模型，以及矩阵分解、ROC分析等时下热点话题。
本书不仅内容丰富，而且图文并茂，无论是新手还是有经验的读者都能从中获益。

Content Description

《机器学习》是全面的机器学习教材之一。书中首先介绍了机器学习的构成要素（任务、模型、特征）和机器学习任务，接着详细分析了逻辑模型（树模型、规则模型）、几何模型（线性模型和基于距离的模型）和概率模型，然后讨论了特征、模型的集成，以及被机器学习研究者称为“实验”的方法。作者不仅使用了已有术语，还引入了一些新的概念，同时提供了大量精选的示例和插图解说。

Author Description

Peter Flach，布里斯托大学人工智能教授，拥有20多年的机器学习教研经验。在高度结构化的数据挖掘以及通过ROC分析来评估和改进机器学习模型方面，Flach是国际领先的研究人员。他还是Machine Learning期刊总编。曾担任2009年ACM知识发现与数据挖掘国际会议、2012年欧洲机器学习与数据挖掘国际会议的程序委员会共同主席。另著有Simply Logical: Intelligent Reasoning by Example。

Comments

“本书写作思路清楚，逻辑性强。作者首先介绍了机器学习的基础知识，然后提供了大量有价值的结论、对若干机器学习技术性能的洞见，以及许多核心算法的高层伪代码，巧妙地引领读者循序渐进地学习。”
——Fernando Berzal，Computing Reviews

“本书条理清晰，对机器学习技术的核心要素进行了全面的论述。作者首先从宏观角度审视机器学习，然后由面到点，深入阐释了各项技术的具体细节。如果你是机器学习领域的专业人士或学生，阅读本书可以帮助你巩固所学的知识。我向所有想认真研究机器学习的人强烈推荐本书！”
——读者评论

“本书抽象、系统地阐述了机器学习的方方面面，让读者能够深入理解常用机器学习技术的原理。这是攻略和实用手册类图书所做不到的。”
——读者评论

Catalogue

绪论机器学习概述 1
第1章机器学习的构成要素9
1．1任务：可通过机器学习解决的问题9
1．1．1探寻结构11
1．1．2性能评价13
1．2模型：机器学习的输出14
1．2．1几何模型14
1．2．2概率模型17
1．2．3逻辑模型22
1．2．4分组模型与评分模型26
1．3特征：机器学习的马达26
1．3．1特征的两种用法28
1．3．2特征的构造与变换29
1．3．3特征之间的交互32
1．4总结与展望33
第2章两类分类及相关任务37
2．1分类39
2．1．1分类性能的评价40
2．1．2分类性能的可视化43
2．2评分与排序46
2．2．1排序性能的评价及可视化48
2．2．2将排序器转化为分类器52
2．3类概率估计54
2．3．1类概率估计量55
2．3．2将排序器转化为概率估计子57
2．4小结与延伸阅读59
第3章超越两类分类61
3．1处理多类问题61
3．1．1多类分类61
3．1．2多类得分及概率65
3．2回归68
3．3无监督学习及描述性学习70
3．3．1预测性聚类与描述性聚类71
3．2．2其他描述性模型74
3．4小结与延伸阅读76
第4章概念学习77
4．1假设空间78
4．1．1最小一般性79
4．1．2内部析取82
4．2通过假设空间的路径84
4．2．1最一般相容假设86
4．2．2封闭概念87
4．3超越合取概念88
4．4可学习性92
4．5小结与延伸阅读94
第5章树模型97
5．1决策树100
5．2排序与概率估计树103
5．3作为减小方差的树学习方法110
5．3．1回归树110
5．3．2聚类树113
5．4小结与延伸阅读115
第6章规则模型117
6．1学习有序规则列表117
6．2学习无序规则集124
6．2．1用于排序和概率估计的规则集128
6．2．2深入探究规则重叠130
6．3描述性规则学习131
6．3．1用于子群发现的规则学习131
6．3．2关联规则挖掘135
6．4一阶规则学习139
6．5小结与延伸阅读143
第7章线性模型145
7．1最小二乘法146
7．1．1多元线性回归150
7．1．2正则化回归153
7．1．3利用最小二乘回归实现分类153
7．2感知机155
7．3支持向量机158
7．4从线性分类器导出概率164
7．5超越线性的核方法168
7．6小结与延伸阅读170
第8章基于距离的模型173
8．1距离测度的多样性173
8．2近邻与范例178
8．3最近邻分类器182
8．4基于距离的聚类184
8．4．1K均值算法186
8．4．2K中心点聚类187
8．4．3silhouette188
8．5层次聚类190
8．6从核函数到距离194
8．7小结与延伸阅读195
第9章概率模型197
9．1正态分布及其几何意义200
9．2属性数据的概率模型205
9．2．1利用朴素贝叶斯模型实现分类206
9．2．2训练朴素贝叶斯模型209
9．3通过优化条件似然实现鉴别式学习211
9．4含隐变量的概率模型214
9．4．1期望最大化算法215
9．4．2高斯混合模型216
9．5基于压缩的模型218
9．6小结与延伸阅读220
第10章特征223
10．1特征的类型223
10．1．1特征上的计算223
10．1．2属性特征、有序特征及数量特征227
10．1．3结构化特征228
10．2特征变换229
10．2．1阈值化与离散化229
10．2．2归一化与标定234
10．2．3特征缺失239
10．3特征的构造与选择240
10．4小结与延伸阅读243
第11章模型的集成245
11．1Bagging 与随机森林246
11．2Boosting247
11．3集成学习进阶250
11．3．1偏差、方差及裕量250
11．3．2其他集成方法251
11．3．3元学习252
11．4小结与延伸阅读252
第12章机器学习的实验255
12．1度量指标的选择256
12．2量指标的获取258
12．3如何解释度量指标260
12．4小结与延伸阅读264
后记路在何方267
记忆要点269
参考文献271

Introduction

人工智能、大数据分析、机器人等领域在近年来日益引人瞩目，而机器学习则是其中一类非常重要的理论和工具。Peter Flach 教授的这部著作可作为机器学习的入门图书，帮助广大迫切希望了解和掌握机器学习的同学和工程师奠定良好的基础。
本书各章节的选题恰到好处，不但对经典机器学习框架中的模型做了非常系统的梳理和分类，涵盖了机器学习基础知识的主要部分，如不同的学习模型、特征、集成学习，而且还对机器学习实验，尤其是机器学习算法的评价（ROC分析）给予了特别的关注，这是十分难能可贵的（在一定程度上弥补了同类教科书的空白）。只要理解了上述内容，相信读者便掌握了机器学习的基本要素，同时有能力进一步就一些更专门、更前沿的主题，如在线学习、主动学习、强化学习、深度学习等，进行更为深入的学习和探索。从这个意义上讲，作者对本书的导论性著作的定位已充分地达成了。
对于如何帮助读者充分理解书中的知识点，作者也予以了充分考虑。书中配有相当数量的图解和实例，旨在突出机器学习理论的直观性。这些内容的数学门槛不高，对工程师和工科学生无疑会有很大的帮助。在各章节中，作者还对每种方法的历史影响进行了介绍，相信会十分有助于提升读者的学习兴趣。作者作为在该领域耕耘多年的研究人员，拥有非常丰富的实践经验，在不少章节中都详尽地分享了实践经验，比如特征和实验这两章。相信这些宝贵的经验会为读者朋友们具体实践机器学习理论带来更好的可操作性。
最后要提到译者段菲的翻译，通篇用语规范、表达准确，总体说来是非常不错的翻译版本。作为计算机视觉领域的优秀研究人员，他本人也在使用机器学习方面有着丰富的研究和实践经验，相信这些都为这本书的翻译质量带来不少保证。
张益民
英特尔（中国）研究院首席研究员

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