Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning (Record no. 178696)
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| fixed length control field | 05108nam a22005175i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-030-83356-5 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240423125516.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 211215s2021 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783030833565 |
| -- | 978-3-030-83356-5 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-3-030-83356-5 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | Q334-342 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TA347.A78 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UYQ |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM004000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UYQ |
| Source | thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kamath, Uday. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 245 10 - TITLE STATEMENT | |
| Title | Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning |
| Medium | [electronic resource] / |
| Statement of responsibility, etc | by Uday Kamath, John Liu. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2021. |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | XXIII, 310 p. 194 illus., 161 illus. in color. |
| Other physical details | online resource. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 1. 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 ## - SUMMARY, ETC. | |
| Summary, etc | This 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 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine Learning. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Liu, John. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer Nature eBook |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9783030833558 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9783030833572 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9783030833589 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1007/978-3-030-83356-5">https://doi.org/10.1007/978-3-030-83356-5</a> |
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| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks-CSE-Springer |
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