hp
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Contents
Preface
1 The Learning Problem
1.1 Problem Setup
1.2 Types of Learning .
1.3 Is Learning Feasible? .
1.4 Error and Noise
1.5 Problems
2 Training versus Testing
2.1 Theory of Generalization .
2.2 Interpreting the Generalization Bound .
2.3 Approximation-Generalization Tradeo˙
2.4 Problems
3 The Linear Model
3.1 Linear Classification
3.2 Linear Regression .
3.3 Logisti
3.4 Nonlinear Transformation .
3.5 Problems
4 Overfitting
4.1 When Does Over˝tting O
4.2 Regularization
4.3 Validation
4.4 Problems
5 Three Learning Principles
5.1 Occam’s Razor
5.2 Sampling Bias
5.3 Data Snooping
5.4 Problems
Epilogue
Further Reading
Appendix Proof of the VC Bound
Notation
Index