本書是機器學習的入門書，深入淺出地講解機器學習的基礎理論與應用，不僅注重給理論添加淺顯易懂的解釋和詳述，而且探討何種創(chuàng)新思維或科學思維可以產生或引導出某個理論，讓學習者在學習理論過程中自然地培養(yǎng)創(chuàng)新思維與科學思維。本書知識點包括回歸分析、k-近鄰算法、決策樹、貝葉斯分類器、支持向量機、模型性能評估、集成學習、降維方法、聚類、EM 算法與高斯混合模型、神經網絡與深度學習等。本書每章都設計手工計算的應用例題，以演示理論解題和計算過程，幫助學習者理解和掌握理論。每章配有編程實踐的實例，不僅示范解題的Python 代碼，還示范解題思路、步驟和結果分析，培養(yǎng)學習者解決實際問題的能力。每章自然地融入科技強國、弘揚中華智慧與文化等內容。此外，每章的習題可鞏固知識，對應的在線課程（中國大學MOOC，課程名：機器學習）可引領學習。 本書的配套教學資源有教學大綱、教學課件、源代碼和案例素材等，讀者可登錄華信教育資源網免費下載；編程實例所用數據集在書中標注了下載途徑。 本書可作為人工智能、計算機相關專業(yè)的教材，或供機器學習理論與應用的學習者使用參考。
王開軍，副教授，碩士生導師，現為福建師范大學計算機與網絡空間安全學院教師，計算智能教研室主任，福建省人工智能學會理事。2008年在西安電子科技大學計算機應用專業(yè)獲得博士學位。2020年在英國阿爾斯特大學計算機系作訪問學者。
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(fā)環(huán)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 基于劃分的聚類方法·········································································127
10.3 基于層次的聚類方法·········································································129
10.4 基于密度的聚類方法·········································································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 卷積神經網絡··················································································175
13.2 循環(huán)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
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