£115.87

Springer Machine Learning for Intelligent Decision Science Book

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Description

Explore the intersection of machine learning and decision science with this comprehensive guide from Springer. This book provides a deep look into machine learning-based decision-making models, offering intelligent, hybrid, and adaptive methods designed to solve complex learning problems. It is specifically built to address decision-making challenges that occur under conditions of uncertainty. By bringing together contributions from data scientists, practitioners, and educators, the text covers a wide range of topics regarding intelligent systems. You will examine recent innovations, current trends, and the practical challenges facing the field today. This resource is designed to help you understand how to apply advanced algorithms to real-world intelligent systems. Whether you are looking to understand new trends or solve specific technical problems, this book serves as a practical tool for navigating the evolving landscape of decision science. It is an essential addition to the library of anyone focused on the development of intelligent systems and advanced algorithmic modeling.

Key Features

Covers machine learning-based decision-making models to help solve complex learning problems.

Provides adaptive and hybrid methods for managing decision-making under conditions of uncertainty.

Includes expert contributions from data scientists, educators, and industry practitioners.

Examines recent innovations and emerging trends within the field of intelligent systems.

Addresses practical challenges encountered when implementing decision science tools.

Serves as a resource for academics, students, researchers, and industry professionals.

Product Specifications

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

Barcode

No barcode data available

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