Adversarial Machine Learning (English, Anthony D. Joseph | Blaine Nelson | Benjamin I. P. Rubinstein)
by Anthony D. Joseph | Blaine Nelson | Benjamin I. P. Rubinstein
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Adversarial Machine Learning by Anthony D. Joseph | Blaine Nelson | Benjamin I. P. Rubinstein is listed under Exam Preparation on BookBajar. Full details, specifications and reader reviews are below.
Book Details
Publisher
Cambridge University Press
Language
English
ISBN-13
9781107043466
ISBN-10
1107043468
Author
Anthony D. Joseph | Blaine Nelson | Benjamin I. P. Rubinstein
About the Book
Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for de…
ISBN: 9781107043466
ISBN-13: 9781107043466
ISBN-10: 1107043468
Reader's Guide
What This Book Covers
- ·The main subject matter of Adversarial Machine Learning
- ·Ideas and detail relevant to Exam Preparation
- ·Context that helps make sense of the material as a whole
Best Suited For
Newcomers to the subject and general readers wanting an accessible starting point.
Why It Stands Out
- ·Ships as a genuine physical copy, checked before dispatch
- ·Covered by BookBajar's 15-day return window
Frequently Asked Questions
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Anthony D. Joseph | Blaine Nelson | Benjamin I. P. Rubinstein is the author of Adversarial Machine Learning. Browse more books by Anthony D. Joseph | Blaine Nelson | Benjamin I. P. Rubinstein on BookBajar.
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What is the ISBN of Adversarial Machine Learning?
Adversarial Machine Learning has ISBN 9781107043466 (ISBN-10: 1107043468). Use this to confirm you're ordering the exact edition listed here.
What can I expect from Adversarial Machine Learning?
About the Book Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments.
Product ID: isbn-9781107043466
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