# Machine-Learning_ZhouZhihua

**Repository Path**: wdy_/Machine-Learning_ZhouZhihua

## Basic Information

- **Project Name**: Machine-Learning_ZhouZhihua
- **Description**: Exercises answers to the book "machine-learning" written by Prof. Zhou Zhihua of Nanjing University
- **Primary Language**: HTML
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-05-26
- **Last Updated**: 2021-05-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Machine-Learning (Zhou Zhihua)（周志华《机器学习》课后练习）
-----
My answers (i.e. ideas, code) for the book "Machine Learning (机器学习)" written by Prof. Zhou Zhihua.

ps. all the code exercises are implemented by **Python** in **eclipse-pydev** env.

for more info: welcome to my blog: [Snoopy_Yuan的博客](http://blog.csdn.net/snoopy_yuan) and [周志华《机器学习》课后习题解答系列](http://blog.csdn.net/snoopy_yuan/article/details/62045353)

(更新中...2017-7-5)
----

### [Ch6.Support Vector Machine (支持向量机)](./ch6_support_vector_machine/) ###

练习包括(exercises include)：

- 支持向量分析实验([code here](./ch6_support_vector_machine/6.2_SVM_test/))；
- 支持向量机与BP网络/C4.5决策树对比实验（experiment of SVM/BP/C4.5）([code here](./ch6_support_vector_machine/6.3_SVM_compare/))；

### [Ch5.Neural Networks (神经网络)](./ch5_neural_networks/) ###

练习包括(exercises include)：

- 激活函数选择考虑（selection of activation function）；
- Sigmoid激活函数与对率回归的联系（the relationships between Sigmoid() and Logistic Regression）；
- BP算法推导（conduction of BP algorithm）；
- 学习率分析（analysis of learning rate in NN training）；
- 标准BP算法和累积BP算法对比实验（comparative experiment of BP）([code here](./ch5_neural_networks/5.5_BP/))；
- BP算法改进（improvement of BP algorithm）([code here](./ch5_neural_networks/5.6_BP_improve/))；
- RBF神经网络实现（implementation of RBF network）([code here](./ch5_neural_networks/5.7_RBF_BP/))；
- SOM神经网络实验（experiment of SOM network）([code here](./ch5_neural_networks/5.8_SOM/))；
- 卷积神经网络实验 - 字符识别（experiment of CNN on MNIST）([code here](./ch5_neural_networks/5.10_CNN/))；

### [Ch4.Decision Tree (决策树)](./ch4_decision_tree/) ###

练习包括(exercises include)：

- 决策树划分选择准则；
- 编程实现ID3算法（implementation of ID3）([code here](./ch4_decision_tree/4.3_ID3/))；
- 编程实现CART算法与剪枝操作（implementation of CART and pruning）([code here](./ch4_decision_tree/4.4_CART/))；
- 多变量决策树生成方式（multivariate decision tree）；
- 非递归决策树生成方法（generation of decision - non-recursive approach using DFS/BFS）

### [Ch3.Linear Model (线性模型)](./ch3_linear_model/) ###

练习包括(exercises include)：

- 分析偏置项b（bias terms）；
- 证明对数似然是凸函数（convex functions）；
- 编程实现对率回归（implementation of logistic regression）([code here](./ch3_linear_model/3.3_logistic_regression_watermelon/))；
- 实验比较k-fold_CV和LOOCV（analysis of cross-validation）([code here](./ch3_linear_model/3.4_cross_validation/))；
- 编程实现线性判别分析（implementation of LDA）（[code here](./ch3_linear_model/3.5_LDA/)）；
- 线性判别分析的非线性拓展（nonlinear stretching of LDA）；
- 最优ECOC编码方式；
- 多分类到二分类分解时类别不平衡的考虑（class-imbalance）。

### [Ch2.Model Evaluation and Selection (模型评估与选择)](./ch2_model_evaluation_and_selection/) ###

练习包括(exercises include)：

- 分层抽样(stratified sampling)划分训练集与测试集；
- 留一法(leave-one-out)与k-折交叉验证法(k-fold cross validation)比较;
- F1值与BEP的关联;
- TPR、FPR、P、R之间的关联;
- AUC推导;
- 错误率与ROC的关系
- ROC曲线与代价曲线(cost-curve)的对应关系;

### [Ch1.Introduction (绪论)](./ch1_introduction/) ###

练习包括(exercises include)：

- 求版本空间(version space)；
- 析合范式(disjunctive normal form)提升假设空间(hypothesis space)；
- 噪声环境(noise)下归纳偏好(inductive bias)考虑；