定 价：49 元
丛书名：
抱歉，电子工业出版社不参与样书赠送活动！
- 作者：王开军
- 出版时间：2025/4/1
- ISBN：9787121500190
- 出 版 社：电子工业出版社
适用读者:本书可作为人工智能、计算机相关专业的教材，或供机器学习理论与应用的学习者使用参考。
- 中图法分类：TP181
- 页码：204
- 纸张：
- 版次：01
- 开本：16开
- 字数：326.399993896484(单位：千字)
本书是机器学习的入门书，深入浅出地讲解机器学习的基础理论与应用，不仅注重给理论添加浅显易懂的解释和详述，而且探讨何种创新思维或科学思维可以产生或引导出某个理论，让学习者在学习理论过程中自然地培养创新思维与科学思维。本书知识点包括回归分析、k-近邻算法、决策树、贝叶斯分类器、支持向量机、模型性能评估、集成学习、降维方法、聚类、EM 算法与高斯混合模型、神经网络与深度学习等。本书每章都设计手工计算的应用例题，以演示理论解题和计算过程，帮助学习者理解和掌握理论。每章配有编程实践的实例，不仅示范解题的Python 代码，还示范解题思路、步骤和结果分析，培养学习者解决实际问题的能力。每章自然地融入科技强国、弘扬中华智慧与文化等内容。此外，每章的习题可巩固知识，对应的在线课程（中国大学MOOC，课程名：机器学习）可引领学习。 本书的配套教学资源有教学大纲、教学课件、源代码和案例素材等，读者可登录华信教育资源网免费下载；编程实例所用数据集在书中标注了下载途径。 本书可作为人工智能、计算机相关专业的教材，或供机器学习理论与应用的学习者使用参考。
王开军，副教授，硕士生导师，现为福建师范大学计算机与网络空间安全学院教师，计算智能教研室主任，福建省人工智能学会理事。2008年在西安电子科技大学计算机应用专业获得博士学位。2020年在英国阿尔斯特大学计算机系作访问学者。
第1章 绪论···································································································1
1.1 机器学习简介·······················································································2
1.2 机器学习方法的分类··············································································5
1.3 机器学习框架·······················································································7
1.4 Python 的机器学习开发环境····································································9
1.5 习题································································································.10
第2章 回归分析··························································································.11
2.1 一元线性回归····················································································.11
2.2 多元线性回归····················································································.14
2.3 多项式回归·······················································································.16
2.4 回归分析的效果评价···········································································.17
2.5 逻辑回归··························································································.19
2.6 实例与编程求解·················································································.22
2.7 习题································································································.26
第3章 k-近邻算法························································································.28
3.1 k-近邻分类算法·················································································.28
3.2 k 值的选取························································································.30
3.3 距离度量··························································································.31
3.4 k-近邻回归算法·················································································.31
3.5 k-近邻算法的性能和特点·····································································.33
3.6 实例与编程求解·················································································.34
3.7 习题································································································.36
第4章 决策树·····························································································.38
4.1 决策树的原理····················································································.38
4.2 决策树的构造算法··············································································.39
4.3 信息熵·····························································································.43
4.4 ID3 算法··························································································.43
4.5 C4.5 算法·························································································.44
4.6 CART 算法·······················································································.45
4.7 决策树的剪枝····················································································.47
4.8 决策树的特点····················································································.48
4.9 实例与编程求解·················································································.48
4.10 习题······························································································.52
第5章 贝叶斯分类器····················································································.54
5.1 概率相关知识····················································································.54
5.2 贝叶斯分类原理·················································································.55
5.3 朴素贝叶斯分类器··············································································.56
5.4 实例与编程求解·················································································.59
5.5 习题································································································.62
第6章 支持向量机·······················································································.63
6.1 二分类问题·······················································································.63
6.2 支持向量机分类原理···········································································.65
6.3 支持向量机中的核方法········································································.71
6.4 支持向量机的特点··············································································.74
6.5 多分类问题·······················································································.75
6.6 支持向量回归····················································································.76
6.7 实例与编程求解·················································································.78
6.8 习题································································································.81
第7章 模型性能评估····················································································.84
7.1 模型性能评估的原理与方法·································································.84
7.2 错误率与准确率·················································································.86
7.3 混淆矩阵··························································································.87
7.4 精确率、召回率与F1 指标···································································.88
7.5 P-R 曲线与ROC 曲线·········································································.90
7.6 实例与编程求解·················································································.93
7.7 习题································································································.96
第8章 集成学习··························································································.98
8.1 集成学习的原理·················································································.98
8.2 Bagging 和随机森林············································································100
8.3 Boosting 和Adaboost···········································································102
8.4 提升树·····························································································104
8.5 实例与编程求解·················································································106
8.6 习题································································································109
第9章 降维算法·························································································.111
9.1 降维的概念······················································································.111
9.2 主成分分析·······················································································112
9.3 奇异值分解·······················································································117
9.4 主成分分析的特点··············································································118
9.5 实例与编程求解·················································································119
9.6 习题································································································124
第10 章 聚类·······························································································125
10.1 聚类的概念·····················································································125
10.2 基于划分的聚类方法·········································································127
10.3 基于层次的聚类方法·········································································129
10.4 基于密度的聚类方法·········································································133
10.5 聚类评估························································································135
10.6 确定数据集中簇的数目······································································138
10.7 几种聚类方法的特点·········································································139
10.8 实例与编程求解···············································································140
10.9 习题······························································································142
第11章 期望-最大化（EM）算法与高斯混合模型···············································144
11.1 极大似然估计法···············································································144
11.2 EM 算法·························································································147
11.3 高斯混合模型··················································································151
11.4 EM 算法和高斯混合模型的特点···························································155
11.5 实例与编程求解···············································································156
11.6 习题······························································································158
第12章 神经网络·························································································160
12.1 神经元与感知机···············································································160
12.2 BP 神经网络····················································································164
12.3 神经网络模型的特点·········································································170
12.4 实例与编程求解···············································································171
12.5 习题······························································································174
第13章 深度学习·························································································175
13.1 卷积神经网络··················································································175
13.2 循环神经网络··················································································179
13.3 生成式对抗网络···············································································184
13.4 几种深度学习方法的特点···································································189
13.5 实例与编程求解···············································································190
13.6 习题······························································································193
参考文献·······································································································195
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