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/https://libri-media.knygos-static.lt/a2cb7531-7cdc-4be8-ad75-094f3fd01321/2)
- Leidėjas: Taylor & Francis Ltd
- Metai: 2026
- Puslapiai: 344
- ISBN-10: 1041067941
- ISBN-13: 9781041067948
- Kalba: Anglų
Machine Learning for Biomedical Engineers (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
This book combines machine learning and biomedical engineering to address practical issues in healthcare and biomedical research concentrating on real-world applications including bioinformatics, customized medicine, medical imaging analysis, disease detection, and health monitoring. It contains case studies and examples that show how various machine learning algorithms are used on biomedical data sets. The ethical issues and difficulties unique to using machine learning in biomedical settings, such as data privacy, algorithm bias, and regulatory compliance are also covered.
- Provides a broad introduction to machine learning in biomedicine and biomedical engineering
- Discusses ethical considerations and explainability pertinent to machine learning in bioengineering
- Explores step-by-step tutorials, coding examples, and real-world case studies
- Reviews feature selection, training and evaluating models, preprocessing data, validation techniques tailored to biomedical data
- Includes MATLAB and Python coding programs
This book is aimed at graduate students and researchers in bioengineering and machine learning.
- Leidėjas: Taylor & Francis Ltd
- Metai: 2026
- Puslapiai: 344
- ISBN-10: 1041067941
- ISBN-13: 9781041067948
- Kalba: Anglų
This book combines machine learning and biomedical engineering to address practical issues in healthcare and biomedical research concentrating on real-world applications including bioinformatics, customized medicine, medical imaging analysis, disease detection, and health monitoring. It contains case studies and examples that show how various machine learning algorithms are used on biomedical data sets. The ethical issues and difficulties unique to using machine learning in biomedical settings, such as data privacy, algorithm bias, and regulatory compliance are also covered.
- Provides a broad introduction to machine learning in biomedicine and biomedical engineering
- Discusses ethical considerations and explainability pertinent to machine learning in bioengineering
- Explores step-by-step tutorials, coding examples, and real-world case studies
- Reviews feature selection, training and evaluating models, preprocessing data, validation techniques tailored to biomedical data
- Includes MATLAB and Python coding programs
This book is aimed at graduate students and researchers in bioengineering and machine learning.
Atsiliepimai