9781107512825, Understanding Machine Learning: From Theory To Algorithms, Shai Shalev-Shwartz (Author), Shai Ben-David (Author), Cambridge University Press, Paperback, English, Worlwide

Shai Shalev-Shwartz is a professor at the Hebrew University of Jerusalem and a leading researcher in machine learning, optimization, and artificial intelligence. He has contributed significantly to online learning, convex optimization, and deep learning. Shai Ben-David is a professor at the University of Waterloo, specializing in theoretical machine learning, statistical learning theory, and clustering. He has worked on foundational aspects of learning algorithms and generalization theory. Together, they co-authored Understanding Machine Learning, a widely used textbook bridging theory and practice. Their research has influenced both academia and industry applications of machine learning.

1. Introduction

Part I: Foundations

1. A Gentle Start
2. A Formal Learning Model
3. Learning via Uniform Convergence
4. The Bias-Complexity Trade-off
5. The VC-Dimension
6. Non-Uniform Learnability
7. The Runtime of Learning

Part II: From Theory to Algorithms

1. Linear Predictors
2. Boosting
3. Model Selection and Validation
4. Convex Learning Problems
5. Regularization and Stability
6. Stochastic Gradient Descent
7. Support Vector Machines
8. Kernel Methods
9. Multiclass, Ranking, and Complex Prediction Problems
10. Decision Trees
11. Nearest Neighbor
12. Neural Networks

Part III: Additional Learning Models

1. Online Learning
2. Clustering
3. Learning with Partial Information

Part IV: Advanced Theory

1. The Sample Complexity of Learning
2. The Complexity of Learning
3. Multiclass Learnability
4. Compression Bounds
5. PAC-Bayes

Appendices

• Technical Lemmas
• Measure Concentration
• Linear Algebra

• Publisher ‏ : ‎ Cambridge University Press; Third edition (1 January 2015)
• Language ‏ : ‎ English
• ISBN-13 ‏ : ‎ 978-1107512825
• Item Weight ‏ : ‎ 0.6 Kg
• Dimensions ‏ : ‎ 20 x 14 x 4 cm