000 05108nam a22005175i 4500
001 978-3-030-83356-5
003 DE-He213
005 20240423125516.0
007 cr nn 008mamaa
008 211215s2021 sz | s |||| 0|eng d
020 _a9783030833565
_9978-3-030-83356-5
024 7 _a10.1007/978-3-030-83356-5
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aKamath, Uday.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aExplainable Artificial Intelligence: An Introduction to Interpretable Machine Learning
_h[electronic resource] /
_cby Uday Kamath, John Liu.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXXIII, 310 p. 194 illus., 161 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1. Introduction to Interpretability and Explainability -- 2. Pre-Model Interpretability and Explainability -- 3. Model Visualization Techniques and Traditional Interpretable Algorithms -- 4. Model Interpretability: Advances in Interpretable Machine Learning -- 5. Post-hoc Interpretability and Explanations -- 6. Explainable Deep Learning -- 7. Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision -- 8. XAI: Challenges and Future.
520 _aThis book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students. --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMU This book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning. --Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYU This is a wonderful book! I’m pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I’ve seen that has up-to-date and well-rounded coverage. Thank you to the authors! --Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics, Duke University. Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyonewho is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level. Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist. --Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder of Explainable AI-XAI Group.
650 0 _aArtificial intelligence.
650 0 _aMachine learning.
650 1 4 _aArtificial Intelligence.
650 2 4 _aMachine Learning.
700 1 _aLiu, John.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030833558
776 0 8 _iPrinted edition:
_z9783030833572
776 0 8 _iPrinted edition:
_z9783030833589
856 4 0 _uhttps://doi.org/10.1007/978-3-030-83356-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178696
_d178696