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Cambridge University Press Cambridge Inference and Learning from Data: Volume 3 - Learning

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Description

Inference and Learning from Data: Volume 3, published by Cambridge University Press, offers a rigorous and accessible introduction to the mathematical and statistical techniques used in modern data-driven learning. This final volume in the three-part series focuses specifically on learning methods, expanding upon the foundational topics established in Volume I. Designed for those studying contemporary methods in inference, this work covers a wide range of essential techniques. Readers will find detailed coverage of least-squares methods, regularization, and online learning. The text also explores advanced topics including kernel methods, feedforward and recurrent neural networks, and meta-learning. It serves as a comprehensive resource for understanding the mathematical frameworks that support current developments in the field.

Key Features

This third volume in the series focuses on learning methods to build upon the foundations established in Volume I.

Cambridge University Press provides a rigorous and engaging approach to mathematical and statistical techniques.

The text covers essential learning methods including least-squares methods and regularization techniques.

Readers can study advanced topics such as online learning, kernel methods, and meta-learning.

The content includes detailed coverage of feedforward and recurrent neural networks for data-driven inference.

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