Understanding Machine Learning - Reading Notes
1.1.
Preface
1.2.
Introduction
1.3.
A Gentle Start
1.3.1.
The Statistical Learning Framework
1.3.2.
Empirical Risk Minimization
1.3.3.
Empirical Risk Minimization with Inductive Bias
1.4.
A Formal Learning Model
1.4.1.
PAC Learning
1.4.2.
General PAC Learning Model
1.5.
Learning via Uniform Convergence
1.5.1.
Uniform Convergence Is Sufficient for Learnability
1.5.2.
Finite Classes Are Agnostic PAC Learnable
1.6.
The Bias-Complexity Tradeoff
1.6.1.
The No-Free-Lunch Theorem
1.6.2.
Error Decomposition
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Preface
Understanding Machine Learning - Reading Notes
My notes for reading the book
Understanding Machine Learning: From Theory to Algorithms
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