Understanding Machine Learning
Table of contents
- Introduction
- Foundations
- A Gentle Start
- A Formal Learning Model
- Learning via Uniform Convergence
- The Bias-Complexity Tradeoff
- The VC-Dimension
- Nonuniform Learnability
- The Runtime of Learning
- From Theory to Algorithms
- Linear Predictors
- Boosting
- Model Selection and Validation
- Convex Learning Problems
- Regularization and Stability
- Stochastic Gradient Descent
- Support Vector Machines
- Kernel Methods
- Multiclass, Ranking, and Complex Prediction Problems
- Decision Trees
- Nearest Neighbor
- Neural Networks
- Additional Learning Models
- Online Learning
- Clustering
- Dimensionality Reduction
- Generative Models
- Feature Selection and Generation
- Advanced Theory
- Rademacher Complexities
- Covering Numbers
- Proof of the Fundamental Theorem of Learning Theory
- Multiclass Learnability
- Compression Bounds
- PAC-Bayes
- Technical Lemmas
- Measure Concentration
- Linear Algebra
| Pages : | 449 |
| Size : | 3.5 MB |
| File type : | |
| Downloads: | 58 |
| Created: | 2022-02-03 |
| License: | For personal or educational use |
| Author(s): | Shai Shalev-Shwartz, Shai Ben-David |
Others machine learning Tutorials
Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
An Introduction to Machine Learning, 2nd Edition
Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
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