This unique book discusses machine understanding (MU). This new branch of classic machine perception research focuses on perception that leads to understanding

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying thei

This is a comprehensive and thought-provoking exploration of the nature of machine understanding, its evaluation, and its implications. The book proposes a new

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simp

This book presents the selected results of research on the further development of the shape understanding system (SUS) described in our previous book titled “