Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches

Author: Antonio Lepore

Publisher: Springer Nature

Published: 2022-10-19

Total Pages: 130

ISBN-13: 3031124022

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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.

Interpretability for Industry 4.0
Interpretability for Industry 4.0

Author: Antonio Lepore

Publisher:

Published: 2022

Total Pages: 0

ISBN-13: 9783031124037

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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
Interpretable Machine Learning

Author: Christoph Molnar

Publisher: Lulu.com

Published: 2020

Total Pages: 320

ISBN-13: 0244768528

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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.

The Economics of Artificial Intelligence
The Economics of Artificial Intelligence

Author: Ajay Agrawal

Publisher: University of Chicago Press

Published: 2024-03-05

Total Pages: 172

ISBN-13: 0226833127

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A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

An Introduction to Statistical Learning
An Introduction to Statistical Learning

Author: Gareth James

Publisher: Springer Nature

Published: 2023-08-01

Total Pages: 617

ISBN-13: 3031387473

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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.

Data Science for Economics and Finance
Data Science for Economics and Finance

Author: Sergio Consoli

Publisher: Springer Nature

Published: 2021

Total Pages: 357

ISBN-13: 3030668916

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This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

Hybrid Metal Additive Manufacturing
Hybrid Metal Additive Manufacturing

Author: Parnika Shrivastava

Publisher: CRC Press

Published: 2023-12-05

Total Pages: 269

ISBN-13: 1003803245

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The text presents the latest research and development, technical challenges, and future directions in the field of hybrid metal additive manufacturing. It further discusses the modeling of hybrid additive manufacturing processes for metals, hybrid additive manufacturing of composite materials, and low-carbon hybrid additive manufacturing processes. THIS BOOK •Presents cutting-edge advancements and limitations in hybrid additive manufacturing technologies. • Discusses fabrication methods and rapid tooling techniques focusing on metals, composites, and alloys. •Highlights the importance of low-carbon additive manufacturing technologies toward achieving sustainability. •Emphasizes the challenges and solutions for integrating additive manufacturing and Industry 4.0 to enable rapid manufacturing of customized and tailored products. • Covers hybrid additive manufacturing of composite materials and additive manufacturing for fabricating high-hardness components. The text discusses the recent advancements in additive manufacturing of high-hardness components and covers important engineering materials such as metals, alloys, and composites. It further highlights defects and post-processing of hybrid additive manufacturing components, sustainability solutions for hybrid additive manufacturing processes, and recycling of machining waste into metal powder feedstock. It will serve as an ideal reference text for senior undergraduate and graduate students, and researchers in fields including mechanical engineering, aerospace engineering, manufacturing engineering, and production engineering.

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance
Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

Author: El Bachir Boukherouaa

Publisher: International Monetary Fund

Published: 2021-10-22

Total Pages: 35

ISBN-13: 1589063953

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This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Author: Wojciech Samek

Publisher: Springer Nature

Published: 2019-09-10

Total Pages: 435

ISBN-13: 3030289540

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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.

The Principles of Deep Learning Theory
The Principles of Deep Learning Theory

Author: Daniel A. Roberts

Publisher: Cambridge University Press

Published: 2022-05-26

Total Pages: 473

ISBN-13: 1316519333

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This volume develops an effective theory approach to understanding deep neural networks of practical relevance.