Interpretability For Industry 4 0 Statistical And Machine Learning Approaches
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Interpretability For Industry 4 0 Statistical And Machine Learning Approaches
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Author : Antonio Lepore
language : en
Publisher: Springer Nature
Release Date : 2022-10-19
Interpretability For Industry 4 0 Statistical And Machine Learning Approaches written by Antonio Lepore and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Mathematics categories.
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
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
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.
A Decade Of Research Activities At The Department Of Industrial Engineering Unina Dii
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Author : Nicola Bianco
language : en
Publisher: Springer Nature
Release Date : 2024-06-01
A Decade Of Research Activities At The Department Of Industrial Engineering Unina Dii written by Nicola Bianco and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-01 with Technology & Engineering categories.
This open access book celebrates the decennial of the Department of Industrial Engineering of Università di Napoli Federico II, Italy. It covers the main research achievements developed at the department in the fields of aerospace, marine, energy, statistical, mechanical and management engineering. Five pre-existing departments merged in 2013, and the research results are here summarized to certify how important it was to join skills, expertise, and projects. The industrial engineering area is huge, but it is now dominated by the need to conceive and analyze new solutions, human and climate oriented to face with the actual challenges which dictate the new paradigm, which evolved from “is it feasible?” to “is it compatible with the environment and the human beings?”. There is still a lot to do, but the contents of this book demonstrate that the first steps have been done. All the researchers of the department have contributed to this book, more than 140 authors, and thus, it isthe collective outcome of the path they were able to perform all together, including administrative officers and technicians. It highlights the international relevance and multidisciplinarity of research at the university as well as the planned research lines for the next years.
Explainable And Interpretable Models In Computer Vision And Machine Learning
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Author : Hugo Jair Escalante
language : en
Publisher: Springer
Release Date : 2018-11-29
Explainable And Interpretable Models In Computer Vision And Machine Learning written by Hugo Jair Escalante and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-29 with Computers categories.
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
Applications In Statistical Computing
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Author : Nadja Bauer
language : en
Publisher: Springer Nature
Release Date : 2019-10-12
Applications In Statistical Computing written by Nadja Bauer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-12 with Computers categories.
This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics. Gathering innovative, high-quality and scientifically relevant contributions, the volume was published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University, on the occasion of his 66th birthday.
Quality In The Era Of Industry 4 0
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Author : Kai Yang
language : en
Publisher: John Wiley & Sons
Release Date : 2023-12-07
Quality In The Era Of Industry 4 0 written by Kai Yang and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-07 with Technology & Engineering categories.
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.
Additive Logistic Regression
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Author : Jerome H. Friedman
language : en
Publisher:
Release Date : 1998
Additive Logistic Regression written by Jerome H. Friedman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Regression analysis categories.
Interpretable Machine Learning With Python
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Author : Serg Masís
language : en
Publisher:
Release Date : 2021-03-26
Interpretable Machine Learning With Python written by Serg Masís and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-26 with categories.
Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models Key Features: Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book Description: Do you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models. The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you'll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What You Will Learn: Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for: This book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.
Empirical Asset Pricing
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Author : Wayne Ferson
language : en
Publisher: MIT Press
Release Date : 2019-03-12
Empirical Asset Pricing written by Wayne Ferson and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-12 with Business & Economics categories.
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
An Introduction To Machine Learning
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Author : Miroslav Kubat
language : en
Publisher: Springer
Release Date : 2017-08-31
An Introduction To Machine Learning written by Miroslav Kubat and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-31 with Computers categories.
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.