Machine Learning Theory
来源：https://mostafa-samir.github.io/
作者：Mostafa Samir
Machine Learning Theory – Part 1: Introduction
- Motivation
- Who is this Series for?
- Prerequisites
- Cautions
- Formalizing the Learning Problem
- The Target Function
- The Hypothesis
- The Loss Function
- The Generalization Error
- Is the Learning Problem Solvable?
- References and Additional Readings
- Share
- Comments
Machine Learning Theory – Part 2: Generalization Bounds
- Independently, and Identically Distributed
- The Law of Large Numbers
- Hoeffding’s inequality
- Generalization Bound: 1st Attempt
- Examining the Independence Assumption
- The Symmetrization Lemma
- The Growth Function
- The VC-Dimension
- The VC Generalization Bound
- Distribution-Based Bounds
- One Inequality to Rule Them All
- References and Additional Readings
- Share
- Comments
Machine Learning Theory – Part 3: Regularization and the Bias-variance Trade-off
- Why rich hypotheses are bad?
- The Bias-variance Decomposition
- A Little Exercise
- Taming the Rich
- References and Additional Readings
- Share
- Comments
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