£121.12

Springer - Trustworthy Machine Learning under Imperfect Data

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Description

Improve your understanding of modern artificial intelligence with Trustworthy Machine Learning under Imperfect Data. Published by Springer, this book addresses the challenges of working with real-world datasets that are often incomplete or noisy. As machine learning models become more common in critical applications, the need for reliability is higher than ever. This text focuses on how to develop systems that remain effective even when the input data is not perfect. It provides a foundation for researchers and professionals looking to navigate the complexities of data quality in AI development. Whether you are studying computer science or working in the field of machine learning, this resource offers insights into building more resilient models. It is a valuable addition to your collection of technical literature on computing and internet technologies.

Key Features

Published by Springer, a leading name in academic and professional technical literature.

Focuses on the specific challenges of machine learning when dealing with imperfect data.

Provides technical knowledge suitable for the field of computer science and AI.

Addresses the necessity of building reliable and trustworthy machine learning systems.

A specialized resource for those studying computing and internet technologies.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
20 October 2025
Listed Since
31 May 2025

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