機器學習理論與應用

第1章 緒論···································································································1
1.1 機器學習簡介·······················································································2
1.2 機器學習方法的分類··············································································5
1.3 機器學習框架·······················································································7
1.4 Python 的機器學習開發環境····································································9
1.5 習題································································································.10
第2章 回歸分析··························································································.11
2.1 一元線性回歸····················································································.11
2.2 多元線性回歸····················································································.14
2.3 多項式回歸·······················································································.16
2.4 回歸分析的效果評價···········································································.17
2.5 邏輯回歸··························································································.19
2.6 實例與編程求解·················································································.22
2.7 習題································································································.26
第3章 k-近鄰算法························································································.28
3.1 k-近鄰分類算法·················································································.28
3.2 k 值的選取························································································.30
3.3 距離度量··························································································.31
3.4 k-近鄰回歸算法·················································································.31
3.5 k-近鄰算法的性能和特點·····································································.33
3.6 實例與編程求解·················································································.34
3.7 習題································································································.36
第4章 決策樹·····························································································.38
4.1 決策樹的原理····················································································.38
4.2 決策樹的構造算法··············································································.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 決策樹的剪枝····················································································.47
4.8 決策樹的特點····················································································.48
4.9 實例與編程求解·················································································.48
4.10 習題······························································································.52
第5章 貝葉斯分類器····················································································.54
5.1 概率相關知識····················································································.54
5.2 貝葉斯分類原理·················································································.55
5.3 樸素貝葉斯分類器··············································································.56
5.4 實例與編程求解·················································································.59
5.5 習題································································································.62
第6章 支持向量機·······················································································.63
6.1 二分類問題·······················································································.63
6.2 支持向量機分類原理···········································································.65
6.3 支持向量機中的核方法········································································.71
6.4 支持向量機的特點··············································································.74
6.5 多分類問題·······················································································.75
6.6 支持向量回歸····················································································.76
6.7 實例與編程求解·················································································.78
6.8 習題································································································.81
第7章 模型性能評估····················································································.84
7.1 模型性能評估的原理與方法·································································.84
7.2 錯誤率與準確率·················································································.86
7.3 混淆矩陣··························································································.87
7.4 精確率、召回率與F1 指標···································································.88
7.5 P-R 曲線與ROC 曲線·········································································.90
7.6 實例與編程求解·················································································.93
7.7 習題································································································.96
第8章 集成學習··························································································.98
8.1 集成學習的原理·················································································.98
8.2 Bagging 和隨機森林············································································100
8.3 Boosting 和Adaboost···········································································102
8.4 提升樹·····························································································104
8.5 實例與編程求解·················································································106
8.6 習題································································································109
第9章 降維算法·························································································.111
9.1 降維的概念······················································································.111
9.2 主成分分析·······················································································112
9.3 奇異值分解·······················································································117
9.4 主成分分析的特點··············································································118
9.5 實例與編程求解·················································································119
9.6 習題································································································124
第10 章 聚類·······························································································125
10.1 聚類的概念·····················································································125
10.2 基於劃分的聚類方法·········································································127
10.3 基於層次的聚類方法·········································································129
10.4 基於密度的聚類方法·········································································133
10.5 聚類評估························································································135
10.6 確定數據集中簇的數目······································································138
10.7 幾種聚類方法的特點·········································································139
10.8 實例與編程求解···············································································140
10.9 習題······························································································142
第11章 期望-最大化（EM）算法與高斯混合模型···············································144
11.1 極大似然估計法···············································································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點···························································155
11.5 實例與編程求解···············································································156
11.6 習題······························································································158
第12章 神經網絡·························································································160
12.1 神經元與感知機···············································································160
12.2 BP 神經網絡····················································································164
12.3 神經網絡模型的特點·········································································170
12.4 實例與編程求解···············································································171
12.5 習題······························································································174
第13章 深度學習·························································································175
13.1 捲積神經網絡··················································································175
13.2 循環神經網絡··················································································179
13.3 生成式對抗網絡···············································································184
13.4 幾種深度學習方法的特點···································································189
13.5 實例與編程求解···············································································190
13.6 習題······························································································193
參考文獻·······································································································195