Master the linear algebra skills essential for careers in AI. For postgraduates and advanced students, this text provides novel approaches to matrix theory, introducing concepts like the gradient vector and Hessian matrix used in machine learning, with key examples and exercises.
This book applies random process theory to physical and biological research. Using risk and storage processes—little used in physics—it explores the first-passage time, introduces a new statistical distribution, and considers the unstudied effects of entropy changes.
Understanding Interactions in Complex Systems
This book explores the complex nature of interactions and the modeling of interactional systems. It shows that all disciplines can be enriched by exploring alternative paradigms, arguing that ignoring the multi-dimensional nature of interactions is not an option.