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Machine Learning Theory

来源:https://mostafa-samir.github.io/

作者:Mostafa Samir

Machine Learning Theory – Part 1: Introduction

  1. Motivation
  2. Who is this Series for?
  3. Prerequisites
  4. Cautions
  5. Formalizing the Learning Problem
    • The Target Function
    • The Hypothesis
    • The Loss Function
    • The Generalization Error
    • Is the Learning Problem Solvable?
  6. References and Additional Readings
    • Share
    • Comments

Machine Learning Theory – Part 2: Generalization Bounds

  1. Independently, and Identically Distributed
  2. The Law of Large Numbers
  3. Hoeffding’s inequality
  4. Generalization Bound: 1st Attempt
  5. Examining the Independence Assumption
  6. The Symmetrization Lemma
  7. The Growth Function
  8. The VC-Dimension
  9. The VC Generalization Bound
  10. Distribution-Based Bounds
  11. One Inequality to Rule Them All
  12. References and Additional Readings
    • Share
    • Comments

Machine Learning Theory – Part 3: Regularization and the Bias-variance Trade-off

  1. Why rich hypotheses are bad?
  2. The Bias-variance Decomposition
    • A Little Exercise
  3. Taming the Rich
  4. References and Additional Readings
    • Share
    • Comments

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Machine Learning Theory

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