Interpretability For Industry 4 0 Statistical And Machine Learning Approaches
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Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches by Antonio Lepore,Biagio Palumbo,Jean-Michel Poggi Pdf
This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.
Interpretable Machine Learning by Christoph Molnar Pdf
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Quality in the Era of Industry 4.0 by Kai Yang Pdf
QUALITY IN THE ERA OF INDUSTRY 4.0 Enables readers to use real-world data from connected devices to improve product performance, detect design vulnerabilities, and design better solutions Quality in the Era of Industry 4.0 provides an insightful guide to harnessing user performance and behavior data through AI and other Industry 4.0 technologies. This transformative approach enables companies to not only optimize products and services in real-time, but also to anticipate and mitigate likely failures proactively. In a succinct and lucid style, the book presents a pioneering framework for a new paradigm of quality management in the Industry 4.0 landscape. It introduces groundbreaking techniques such as utilizing real-world data to tailor products for superior fit and performance, leveraging connectivity to adapt products to evolving needs and use-cases, and employing cutting-edge manufacturing methods to create bespoke, cost-effective solutions with greater efficiency. Case examples featuring applications from the automotive, mobile device, home appliance, and healthcare industries are used to illustrate how these new quality approaches can be used to benchmark the product’s performance and durability, maintain smart manufacturing, and detect design vulnerabilities. Written by a seasoned expert with experience teaching quality management in both corporate and academic settings, Quality in the Era of Industry 4.0 covers topics such as: Evolution of quality through industrial revolutions, from ancient times to the first and second industrial revolutions Quality by customer value creation, explaining differences in producers, stakeholders, and customers in the new digital age, along with new realities brought by Industry 4.0 Data quality dimensions and strategy, data governance, and new talents and skill sets for quality professionals in Industry 4.0 Automated product lifecycle management, predictive quality control, and defect prevention using technologies like smart factories, IoT, and sensors Quality in the Era of Industry 4.0 is a highly valuable resource for product engineers, quality managers, quality engineers, quality consultants, industrial engineers, and systems engineers who wish to make a participatory approach towards data-driven design, economical mass-customization, and late differentiation.
Author : Donald Michie,D. J. Spiegelhalter,C. C. Taylor Publisher : Prentice Hall Page : 312 pages File Size : 45,5 Mb Release : 1994 Category : Classification ISBN : UCSD:31822019003581
Explainable and Interpretable Models in Computer Vision and Machine Learning by Hugo Jair Escalante,Sergio Escalera,Isabelle Guyon,Xavier Baró,Yağmur Güçlütürk,Umut Güçlü,Marcel van Gerven Pdf
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations
Interpretable Machine Learning and Generative Modeling with Mixed Tabular Data by Kristin Blesch Pdf
Explainable artificial intelligence or interpretable machine learning techniques aim to shed light on the behavior of opaque machine learning algorithms, yet often fail to acknowledge the challenges real-world data imposes on the task. Specifically, the fact that empirical tabular datasets may consist of both continuous and categorical features (mixed data) and typically exhibit dependency structures is frequently overlooked. This work uses a statistical perspective to illuminate the far-reaching implications of mixed data and dependency structures for interpretability in machine learning. Several interpretability methods are advanced with a particular focus on this kind of data, evaluating their performance on simulated and real data sets. Further, this cumulative thesis emphasizes that generating synthetic data is a crucial subroutine for many interpretability methods. Therefore, this thesis also advances methodology in generative modeling concerning mixed tabular data, presenting a tree-based approach for density estimation and data generation, accompanied by a user-friendly software implementation in the Python programming language.
Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning by Uday Kamath,John Liu Pdf
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 anyone who 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.
An Introduction to Statistical Learning by Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani,Jonathan Taylor Pdf
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Insights in Metabolomics: 2021 by Wolfram Weckwerth Pdf
We are now entering the third decade of the 21st Century, and, especially in the last years, the achievements made by scientists have been exceptional, leading to major advancements in the fast-growing field of Metabolomics. Frontiers has organized a series of Research Topics to highlight the latest advancements in science in order to be at the forefront of science in different fields of research. This editorial initiative of particular relevance, led by Dr Wolfram Weckwerth, Specialty Chief Editor of the Metabolomics section, is focused on new insights, novel developments, current challenges, latest discoveries, recent advances and future perspectives in the field of Metabolomics.
ECML PKDD 2020 Workshops by Irena Koprinska,Michael Kamp,Annalisa Appice,Corrado Loglisci,Luiza Antonie,Albrecht Zimmermann,Riccardo Guidotti,Özlem Özgöbek,Rita P. Ribeiro,Ricard Gavaldà,João Gama,Linara Adilova,Yamuna Krishnamurthy,Pedro M. Ferreira,Donato Malerba,Ibéria Medeiros,Michelangelo Ceci,Giuseppe Manco,Elio Masciari,Zbigniew W. Ras,Peter Christen,Eirini Ntoutsi,Erich Schubert,Arthur Zimek,Anna Monreale,Przemyslaw Biecek,Salvatore Rinzivillo,Benjamin Kille,Andreas Lommatzsch,Jon Atle Gulla Pdf
This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License.
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by Wojciech Samek,Grégoire Montavon,Andrea Vedaldi,Lars Kai Hansen,Klaus-Robert Müller Pdf
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Multidisciplinary Approach in Research Area (Volume-12) by Chief Editor- Biplab Auddya, Editor- Dr.Ranjeet Singh, Dr. Archana Tripathi, Dr.Lata Sharma, Dr.S.Tamilselvi, Dr.Sarika.G, V.Geetha Pdf
Deep Learning in Personalized Healthcare and Decision Support by Harish Garg,Jyotir Moy Chatterjee Pdf
Deep Learning in Personalized Healthcare and Decision Support discusses the potential of deep learning technologies in the healthcare sector. The book covers the application of deep learning tools and techniques in diverse areas of healthcare, such as medical image classification, telemedicine, clinical decision support system, clinical trials, electronic health records, precision medication, Parkinson disease detection, genomics, and drug discovery. In addition, it discusses the use of DL for fraud detection and internet of things. This is a valuable resource for researchers, graduate students and healthcare professionals who are interested in learning more about deep learning applied to the healthcare sector. Although there is an increasing interest by clinicians and healthcare workers, they still lack enough knowledge to efficiently choose and make use of technologies currently available. This book fills that knowledge gap by bringing together experts from technology and clinical fields to cover the topics in depth. Discusses the application of deep learning in several areas of healthcare, including clinical trials, telemedicine and health records management Brings together experts in the intersection of deep learning, medicine, healthcare and programming to cover topics in an interdisciplinary way Uncovers the stakes and possibilities involved in realizing personalized healthcare services through efficient and effective deep learning technologies
