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Introduction
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Classroom Contents
Generalization Theory in Machine Learning
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- 1 Introduction
- 2 Traditional Machine Learning
- 3 Deep Learning
- 4 Deep Learning Everywhere
- 5 Image Classification
- 6 ImageNet
- 7 Classification
- 8 Classification Notation
- 9 Classification Loss
- 10 Hypothesis Classes
- 11 Kernel Methods
- 12 Gaussian Kernel
- 13 Quadratic Loss
- 14 Summary
- 15 Statistical Learning Theory
- 16 Curse of Dimensionality
- 17 Gap for Learning
- 18 Proof
- 19 First Inequality
- 20 Defining Complexity
- 21 Empirical Complexity
- 22 NonEmpirical Complexity
- 23 The Gap
- 24 McDermotts Inequality
