台湾大学林轩田机器学习笔记
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
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机器学习技法
机器学习技法
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