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Springer Machine Learning for Intelligent Decision Science - Springer

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Description

Machine Learning for Intelligent Decision Science explores decision-making models powered by machine learning, presenting hybrid and adaptive methods for solving complex learning and decision problems in uncertain conditions. This comprehensive resource from Springer combines contributions from data scientists, practitioners and educators to cover intelligent systems across decision science. The book examines recent innovations, emerging trends and real-world challenges that professionals face when implementing machine learning solutions. Whether you are an academic, student, researcher or industry professional, this guide offers practical insights and theoretical foundations for applying machine learning to improve organisational decision-making and problem-solving capabilities.

Key Features

Machine Learning for Intelligent Decision Science presents hybrid and adaptive methods designed specifically for complex decision-making under uncertainty.

The book features contributions from experienced data scientists, practitioners and educators providing real-world perspectives on intelligent systems.

Coverage spans recent innovations and trends in decision science, keeping readers current with the latest developments in machine learning applications.

Practical insights and challenges discussed throughout make this resource valuable for implementing machine learning solutions in professional settings.

Suitable for academics, students, researchers and professionals seeking comprehensive understanding of decision-making through machine learning approaches.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
03 April 2020
Listed Since
07 February 2020

Barcode

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