Foundations of Machine Learning

Livro digital

Título:
Foundations of Machine Learning

Autor:
Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

Categoria:
Tecnologia > IA

Doador:
Raffaello D. N.

Sinopse:
A rigorous introduction to machine learning that moves from the basic question of what learning means to formal guarantees, generalization, and the practical structure of modern predictive systems. Even in the opening chapters and table of contents, the book makes its scope clear by connecting PAC learning, learning scenarios, and generalization instead of treating them as isolated buzzwords. From there, the material deepens into the core mathematical tools that shape the field, including Rademacher complexity, VC-dimension, kernel methods, regularization, support vector machines, boosting, decision trees, neural networks, structured prediction, and online learning. The result is a coherent path through theory and algorithms, showing how statistical learning ideas connect to real model design and analysis. Written by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, this second edition is especially valuable for readers who want more than a surface tour of AI. It is a demanding but highly rewarding foundation for students, researchers, and practitioners who want to understand not only how machine learning methods work, but why their guarantees and limitations matter.