Understanding Machine Learning - Reading Notes
1.1. Preface
1.2. Introduction
1.3. A Gentle Start
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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A Formal Learning Model

A Formal Learning Model

In this chapter, we formally define the PAC learning model, which was briefly introduced in the last chapter.

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