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Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David is a comprehensive guide to the theoretical foundations and practical algorithms of machine learning. It covers fundamental concepts such as supervised and unsupervised learning, PAC learning, and VC dimension, along with optimization techniques and neural networks. The book balances mathematical rigor with practical insights, making it suitable for students, researchers, and professionals. It includes numerous exercises to reinforce learning. Published by Cambridge University Press, this book is widely used in academic courses and self-study worldwide.
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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
Part II: From Theory to Algorithms
Part III: Additional Learning Models
Part IV: Advanced Theory
Appendices
About The Book
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David is a comprehensive guide to the theoretical foundations and practical algorithms of machine learning. It covers fundamental concepts such as supervised and unsupervised learning, PAC learning, and VC dimension, along with optimization techniques and neural networks. The book balances mathematical rigor with practical insights, making it suitable for students, researchers, and professionals. It includes numerous exercises to reinforce learning. Published by Cambridge University Press, this book is widely used in academic courses and self-study worldwide.
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