The underlying technique is based on verifying requirements through model checking. The book explains the syntax of mCRL2 and offers modelling tips and tricks.
ISBN:
(数字)9783031230080
ISBN:
(纸本)9783031230073;9783031230103
The underlying technique is based on verifying requirements through model checking. The book explains the syntax of mCRL2 and offers modelling tips and tricks.
The area of Explainable Artificial Intelligence (XAI) is concerned with providing methods and tools to improve the interpretability of black-box learning models. While several approaches exist to generate explanations...
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ISBN:
(数字)9783031890222
ISBN:
(纸本)9783031890215
The area of Explainable Artificial Intelligence (XAI) is concerned with providing methods and tools to improve the interpretability of black-box learning models. While several approaches exist to generate explanations, they are often lacking robustness, e.g., they may produce completely different explanations for similar events. This phenomenon has troubling implications, as lack of robustness indicates that explanations are not capturing the underlying decision-making process of a model and thus cannot be trusted.
This book aims at introducing Robust Explainable AI, a rapidly growing field whose focus is to ensure that explanations for machine learning models adhere to the highest robustness standards. We will introduce the most important concepts, methodologies, and results in the field, with a particular focus on techniques developed for feature attribution methods and counterfactual explanations for deep neural networks.
As prerequisites, a certain familiarity with neural networks and approaches within XAI is desirable but not mandatory. The book is designed to be self-contained, and relevant concepts will be introduced when needed, together with examples to ensure a successful learning experience.
The provided working code for Python solutions, together with easy-to-follow examples, will reinforce the reader’s immediate understanding of the topic. Accompanying data sets and Python programs are also available o...
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ISBN:
(数字)9783030973711
ISBN:
(纸本)9783030973704
The provided working code for Python solutions, together with easy-to-follow examples, will reinforce the reader’s immediate understanding of the topic. Accompanying data sets and Python programs are also available online. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis.
Ce livre est constitué de deux grandes parties : la première est dédiée aux concepts principaux du logiciel R. Elle permettra de s'attaquer sereinement à un problème de nature statist...
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Ce livre est constitué de deux grandes parties : la première est dédiée aux concepts principaux du logiciel R. Elle permettra de s'attaquer sereinement à un problème de nature statistique sans en être limité par les aspects informatiques ; la seconde traite en détails des méthodes statistiques classiques que sont les statistiques descriptives, la simulation de variables aléatoires, les intervalles de confiance et les tests d'hypothèses, la régression linéaire et l'ANOVA, y compris à mesures répétées, etc.
Cet ouvrage peut servir de manuel de cours pour des utilisateurs d'un niveau débutant à avancé, et ceci sur les environnements Windows, Macintosh ou Linux. Il est agrémenté de nombreux exercices et travaux pratiques, dont les corrections sont mises à disposition des enseignants sur un site Internet associé au livre. Il peut aussi servir de bible dans laquelle il devient aisé de retrouver l'instruction R nécessaire à la résolution d'un problème donné.
The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicabi...
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ISBN:
(数字)9783031316364
ISBN:
(纸本)9783031316357;9783031316388
The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail.;As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. Only a basic familiarity with advanced calculus, linear algebra and statistics is assumed, making the material accessible to students at the advanced undergraduate level.
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