Machine Learning

该库用于常见机器学习算法的理论讲解及其代码实现。文章均为原创文章，代码均为贡献者自己实现。

Method	Theory	Code	Manager	e-mail
0 绪论(Introduction)	Theory		@Alex	[email protected]
0.1 性能度量(Performance Metrics)	Theory		@Alex	[email protected]
1 线性回归(Linear Regression)		Code	@Alex	[email protected]
2 逻辑回归(Logistics Regression)	Theory		@Aristotle-wu	[email protected]
3 决策树(Decision Tree)	Theory		@Alex	[email protected]
4 XGBoost	Theory		@Alex	[email protected]
5 支持向量机(Support Vector Machine)	Theory	Code	@Alex	[email protected]
6 贝叶斯(Bayes)	Theory	Code	@Alex	[email protected]
7 神经网络(Neural Network)	Theory	Code	@Alex	[email protected]
7.1 Dropout	Theory		@Alex	[email protected]
8 聚类(Clustering)	Theory		@Alex	[email protected]
9 降维与度量学习
9.1 kNN	Theory	Code	@Alex	[email protected]
9.2 PCA		Code	@Alex	[email protected]
9.3 MDS		Code	@Alex	[email protected]
9.4 Isomap		Code	@Alex	[email protected]
10 集成学习(ensemble learning)	Theory		@Alex	[email protected]
10.1 Boosting	Theory		@Alex	[email protected]
10.2 Bagging and RF(随机森林)	Theory		@Alex	[email protected]
11 特征选择与稀疏学习	Theory		@Alex	[email protected]
12 计算学习理论	Theory		@Alex	[email protected]
13 卷积神经网络		Code	@Alex	[email protected]

Appendix

欢迎关注微信公众号：繁星的人工智能厨房