caijiqhx / MLFoundations

National Taiwan University, Hsuan-Tien Lin, Machine Learning Foundations

Machine Learning Foundations

National Taiwan University, Hsuan-Tien Lin(林轩田)

菜鸡的机器学习入门

课程大纲

When Can Machines Learn?

1. The Learning Problem [机器学习问题]
2. Learning to Answer Yes/No [二元分析]
3. Types of Learning [各种机器学习问题]
4. Feasibility of Learning [机器学习的可行性]

Why Can Machines Learn?

5. Training versus Testing [训练与测试]
6. Theory of Generalization [举一反三的一般化理论]
7. The VC Dimension [VC维度]
8. Noise and Error [噪音与错误]

How Can Machine Learn?

9. Linear Regression [线性回归]
10. Logistic Regression [逻辑回归]
11. Linear Models for Classification [分类的线性模型]
12. Nonlinear Transformation [非线性变换]

How Can Machine Learn Better?

13. Hazard of Overfitting [过度拟合的危险]
14. Regularization [正则化]
15. Validation [验证方式]
16. Three Learning Principles [三个机器学习原则]

预备知识

hw0.pdf