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Cambridge University Press - Random Matrix Methods for ML Book

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£60 today · all-time low £60 (Oct 2026) · usually £65

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£64.99 £59.56 £60.74 £61.93 £63.11 £64.30 £65.48 09 July 2026 31 July 2026 22 August 2026 13 September 2026 06 October 2026

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Description

Master the complexities of large-dimensional data with Random Matrix Methods for Machine Learning. Published by Cambridge University Press, this book provides a unified theory designed specifically for modern machine learning applications. By leveraging concentration and universality phenomena, readers gain a precise understanding of the core mechanisms that drive real-world algorithms. The text begins with a thorough introduction to the theoretical basics of random matrices. This foundational knowledge supports a wide range of practical applications, making it a valuable resource for those working with high-dimensional datasets. Whether you are studying Support Vector Machines (SVMs), semi-supervised learning, or unsupervised spectral clustering, this book offers the mathematical depth required to improve algorithmic performance. Beyond standard classification, the content covers graph methods and neural networks. It is an essential guide for researchers and students looking to exploit mathematical phenomena to better understand and enhance the efficiency of machine learning models.

Key Features

Unified theory of random matrices designed for practical machine learning applications and large-dimensional data analysis.

Exploits concentration and universality phenomena to help you understand and improve real-world algorithm mechanisms.

Includes a thorough introduction to theoretical basics to build a strong foundation for advanced study.

Covers a wide scope of applications including SVMs, semi-supervised learning, and unsupervised spectral clustering.

Provides mathematical insights applicable to graph methods and neural network research.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
21 July 2022
Listed Since
16 April 2022

Barcode

No barcode data available

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