# 机器学习技法
- 台湾大学林轩田机器学习笔记
- 机器学习基石
- 1 -- The Learning Problem
- 2 -- Learning to Answer Yes/No
- 3 -- Types of Learning
- 4 -- Feasibility of Learning
- 5 -- Training versus Testing
- 6 -- Theory of Generalization
- 7 -- The VC Dimension
- 8 -- Noise and Error
- 9 -- Linear Regression
- 10 -- Logistic Regression
- 11 -- Linear Models for Classification
- 12 -- Nonlinear Transformation
- 13 -- Hazard of Overfitting
- 14 -- Regularization
- 15 -- Validation
- 16 -- Three Learning Principles
- 机器学习技法
- 1 -- Linear Support Vector Machine
- 2 -- Dual Support Vector Machine
- 3 -- Kernel Support Vector Machine
- 4 -- Soft-Margin Support Vector Machine
- 5 -- Kernel Logistic Regression
- 6 -- Support Vector Regression
- 7 -- Blending and Bagging
- 8 -- Adaptive Boosting
- 9 -- Decision Tree
- 10 -- Random Forest
- 11 -- Gradient Boosted Decision Tree
- 12 -- Neural Network
- 13 -- Deep Learning
- 14 -- Radial Basis Function Network
- 15 -- Matrix Factorization
- 16（完结） -- Finale