Practicing Trustworthy Machine Learning

By:

Pruksachatkun, Yada

By:

Publication details: Mumbai: Shroff Publishers & Distributors Pvt. Ltd., 2023Description: 274ISBN:

9789355422194

Subject(s):

Machine Learning

DDC classification:

006.31 PRU

Summary: With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world.

List(s) this item appears in: New Arrivals for the Month of September - 2023

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Alliance College of Engineering and Design	CSE & IT	006.31 PRU (Browse shelf(Opens below))	Available		E12489

Total holds: 0

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Previous	006.31 PRA Machine Learning Using Python	006.31 PRA Machine Learning Using Python	006.31 PRO Applied Machine Learning and AI for Engineers	006.31 PRU Practicing Trustworthy Machine Learning	006.31 SHA Machine Learning Based Prediction Techniques	006.31 SIN Statistics For Machine Learning: Implement Statistical Methods Used in Machine Learning Using Python	006.31 SIT AI at the Edge: Solving Real-World Problems with Embedded Machine Learning	Next

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable.

Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world.

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