الصفحة 1
الصفحة 1
Explainable Artificial Intelligence : An Introduction to Interpretable Machine Learning
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.
Engineering Multi-Agent Systems ; 7th International Workshop, EMAS 2019, Montreal, QC, Canada, May 13–14, 2019, Revised Selected Papers
This book constitutes the thoroughly refereed post-conference proceedings of the 7th International Workshop on Engineering Multi-Agent Systems, EMAS 2019, held in Montreal, QC, Canada, in May 2019. The 13 revised full papers presented in this book were carefully selected and reviewed from 20 submissions. The papers are grouped in the following topical sections: Multi-Agent Interaction and Organization; Simulation; Social Awareness and Explainability; Learning and Reconfiguration; and Implementation Techniques and Tools.
Machine learning for civil and environmental engineers : A practical approach to data-driven analysis, explainability, and causality
Introduces engineers and engineering students to the applications of artificial intelligence (AI), machine learning (ML), and machine intelligence (MI) in relation to civil and environmental engineering projects and problems, presenting state-of-the-art methodologies and techniques to develop and implement algorithms in the engineering domain.
