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Springer Probability for Statistics and Machine Learning Book

Amazon

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Description

Master the foundations of modern mathematics with this comprehensive guide from Springer. This book offers a versatile and lucid treatment of classic and modern probability theory, bridging the gap between statistical theory and essential machine learning tools. Designed for accessibility, it uses motivating discussions to explain complex concepts clearly. With 20 detailed chapters, this text provides a unified approach to probability and statistics. It is built for deep learning and practice, featuring 423 worked out examples and 808 exercises to reinforce your understanding. Whether you are studying core statistical theory or advanced machine learning applications, this Springer Texts in Statistics volume serves as a complete resource for students and professionals.

Key Features

Covers 20 chapters ranging from fundamental probability theory to advanced machine learning tools.

Includes 423 worked out examples to help clarify complex mathematical concepts through practical application.

Provides 808 exercises to facilitate rigorous practice and mastery of the subject matter.

Integrates classic probability theory with modern statistical theory and machine learning topics.

Features a detailed bibliography for further research and academic study within the field.

Written in an accessible style by Springer to support both foundational and advanced learning.

Product Specifications

Format
paperback
Domain
Amazon UK
Publication Date
14 July 2013
Listed Since
07 June 2013

Barcode

No barcode data available

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