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Machine-Learning-Zhou_Zhihua

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《机器学习》（周志华）算法实现笔记 - The notes of the book Machine Learning (author: Zhou Zhihua)

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# 机器学习（周志华）
# Machine Learning (Author: Prof. Zhou Zhihua)

This repository contains my personal notes and codes for the book of [_Machine Learning_](http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/MLbook2016.htm "Book information") written by [Prof. Zhou Zhihua](http://cs.nju.edu.cn/zhouzh/ "Author Personal Website").

## Supervised Learning
1. [Linear Model from Stratch](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Linear)
   - [Logistic Regresssion from Stratch](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Linear/LogisticRegression) 
   - [Linear Discrimination Analysis (LDA) from Stratch](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Linear/LDA) 
2. [Decision Tree from Stratch](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DecisionTree/)
   - [Iterative Dichotomiser 3 (ID3) Decision Tree](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DecisionTree/ID3)
   - [CART Decision Tree with Pre- and Post- Pruning](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DecisionTree/CART)
   - [Decision Tree Implementation with Non-Recursive Approach](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DecisionTree/Non-Recursive-Tree)
3. [Neural Networks from Stratch](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/NeuralNetworks)
   - [Standard and Accumulated Back-Propagation](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/NeuralNetworks/BackPropagation)
4. [Support Vector Machine from Stratch](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/SVM/)
5. [Bayes Classifier from Stratch](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Bayes)
   - [Naïve Bayes Classifier using Laplacian Correction](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Bayes/Nai%CC%88veBayes)
   - [Averaged One-Dependent Estimator (AODE) of Semi-Naïve Bayes Classifier](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Bayes/AODE)
6. [Ensemble Learning from Stratch](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/EnsembleLearning)
   - [AdaBoost](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/EnsembleLearning/AdaBoost)
   - [Bagging of Decision Stamps](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/EnsembleLearning/Bagging)
7. [Dimension Deduction from Stratch](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DimensionDeduction)
   - [K Nearest Neighbors (KNN)](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DimensionDeduction/KNN)
8. [Feature Selection from Stratch](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/FeatureSelection)
   - [Relief Algorithm](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/FeatureSelection/Relief)
   
## Unsupervised Learning
9. [Clustering from Stratch](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Clustering)
   - [K-Means Algorithm](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Clustering/Kmeans)
   - [Automatic K-Means Algorithm](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Clustering/Automatic-Kmeans)

## Example Codes in Jupyter Notebook for Book Exercise
### [Chapter 3. Linear Model](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Linear)
- [Problem 3.3](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Linear/LogisticRegression/3.3.ipynb) - Implementation of **Logistic Regression** 
- [Problem 3.5](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Linear/LDA/3.5.ipynb) - Implementation of **Linear Discrimination Analysis (LDA)**

### [Chapter 4. Decision Tree](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DecisionTree/)
- [Problem 4.3](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DecisionTree/ID3/4.3.ipynb) - Implementation of **Iterative Dichotomiser 3 (ID3)** Decision Tree
- [Problem 4.4](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DecisionTree/CART/4.4.ipynb) - Implementation of **CART** Decision Tree with Pruning
- [Problem 4.7](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DecisionTree/Non-Recursive-Tree/4.7.ipynb) - Decision Tree Implementation with Non-Recursive Approach

### [Chapter 5. Neural Networks](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/NeuralNetworks)
- [Problem 5.5](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/NeuralNetworks/BackPropagation/5.5.ipynb) - Implementation of Standard and Accumulated **Back-Propagation**

### [Chapter 6. Support Vector Machine](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/SVM/)
- [Problem 6.2](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/SVM/SVM/6.2.ipynb) - Implementation of **Support Vector Machine** using Kernels

### [Chapter 7. Bayes Classifier](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Bayes)
- [Problem 7.3](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Bayes/Nai%CC%88veBayes/7.3.ipynb) - Implementation of **Naïve Bayes Classifier** using Laplacian Correction
- [Problem 7.6](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Bayes/AODE/7.6.ipynb) - Implementation of **Averaged One-Dependent Estimator (AODE)** of Semi-Naïve Bayes Classifier

### [Chapter 8. Ensemble Learning](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/EnsembleLearning)
- [Problem 8.3](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/EnsembleLearning/AdaBoost/8.3.ipynb)- Implementation of **AdaBoost** 
- [Problem 8.5](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/EnsembleLearning/Bagging/8.5.ipynb) - Implementation of **Bagging** for Decision Stamps

### [Chapter 9. Clustering](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Clustering/)
- [Problem 9.4](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Clustering/Kmeans) - Implementation of **K-Means** Algorithm
- [Problem 9.10](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/Clustering/Automatic-Kmeans) - Implementation of **K-Mean with Automatically K Selection** 
### [Chapter 10. Dimension Deduction](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DimensionDeduction)
- [Problem 10.1](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/DimensionDeduction/KNN) - Implementation of **K Nearest Neighbors** Algorithm (KNN)

### [Chapter 11. Feature Selection](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/FeatureSelection)
- [Problem 11.1](https://github.com/Hatchin/Machine-Learning-Zhou_Zhihua/tree/master/FeatureSelection/Relief) - Implementation of **Relief** Algorithm for Feature-Selection in Binary Classification Problem

Machine-Learning-Zhou_Zhihua

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