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https://github.com/hanmq/MachineLearning_Zhouzhihua_ProblemSets

Exercises answers to the book "machine-learning" written by Zhou Zhihua。周志华《机器学习》课后习题,个人解答。各算法都拿numpy和pandas实现了一遍
https://github.com/hanmq/MachineLearning_Zhouzhihua_ProblemSets

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Exercises answers to the book "machine-learning" written by Zhou Zhihua。周志华《机器学习》课后习题,个人解答。各算法都拿numpy和pandas实现了一遍

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# 机器学习——周志华 (西瓜书)课后习题

习题解答中有大量Latex公式,为了方便阅读推荐安装[MathJax](https://chrome.google.com/webstore/detail/mathjax-plugin-for-github/ioemnmodlmafdkllaclgeombjnmnbima)

在[知乎上](https://zhuanlan.zhihu.com/c_1013850291887845376)也有同步更新,如何不能科学上网可以去知乎上看看。

---
习题和涉及算法实现:
### 第二章 模型评估与选择
[习题](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch2--%E6%A8%A1%E5%9E%8B%E8%AF%84%E4%BC%B0%E4%B8%8E%E9%80%89%E6%8B%A9/ch2%E4%B9%A0%E9%A2%98.md)

### 第三章 线性模型
[习题](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch3--%E7%BA%BF%E6%80%A7%E6%A8%A1%E5%9E%8B/ch3%E4%B9%A0%E9%A2%98.md)

[LR-逻辑回归](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/tree/master/ch3--%E7%BA%BF%E6%80%A7%E6%A8%A1%E5%9E%8B/3.3)
[LDA-线性判别分析](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/tree/master/ch3--线性模型/3.5)

### 第四章 决策树
[习题](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch4--%E5%86%B3%E7%AD%96%E6%A0%91/ch4%E4%B9%A0%E9%A2%98.md)

[决策树](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/tree/master/ch4--%E5%86%B3%E7%AD%96%E6%A0%91/4.3-4.4)

### 第五章 神经网络
[习题](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch5--%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/ch5%E4%B9%A0%E9%A2%98.md)

[BP神经网络](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/tree/master/ch5--%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/5.5-5.6)
[RBF-径向基神经网络](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/tree/master/ch5--%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/5.7)

### 第六章 支持向量机
[习题](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch6--%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA/ch6%E4%B9%A0%E9%A2%98.md)

[SVM-支持向量机](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/tree/master/ch6--%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA/mySVM)

### 第七章 贝叶斯分类
[习题](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch7--%E8%B4%9D%E5%8F%B6%E6%96%AF%E5%88%86%E7%B1%BB/ch7%E4%B9%A0%E9%A2%98.md)

[朴素贝叶斯](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/tree/master/ch7--%E8%B4%9D%E5%8F%B6%E6%96%AF%E5%88%86%E7%B1%BB/7.3)
[半朴素贝叶斯-AODE](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/tree/master/ch7--%E8%B4%9D%E5%8F%B6%E6%96%AF%E5%88%86%E7%B1%BB/7.6)

### 第八章 集成学习
[习题](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch8--%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0/ch8%E4%B9%A0%E9%A2%98.md)

[AdaBoost](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch8--%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0/8.3-AdaBoost.py)
[决策树桩Bagging](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch8--%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0/8.5-StumpBagging.py)

### 第九章 聚类
[习题](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch9--%E8%81%9A%E7%B1%BB/ch9%E4%B9%A0%E9%A2%98.md)

[KMeans](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch9--%E8%81%9A%E7%B1%BB/9.4-KMeans.py)

### 第十章 降维与度量学习

[习题](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch10--%E9%99%8D%E7%BB%B4%E4%B8%8E%E5%BA%A6%E9%87%8F%E5%AD%A6%E4%B9%A0/ch10%E4%B9%A0%E9%A2%98.md)

[KNN](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch10--%E9%99%8D%E7%BB%B4%E4%B8%8E%E5%BA%A6%E9%87%8F%E5%AD%A6%E4%B9%A0/10.1-KNN.py)
[PCA](https://github.com/han1057578619/MachineLearning_Zhouzhihua_ProblemSets/blob/master/ch10--%E9%99%8D%E7%BB%B4%E4%B8%8E%E5%BA%A6%E9%87%8F%E5%AD%A6%E4%B9%A0/10.6-PCA.py)