SUMMARY.md

• 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