Algorithms in Machine Learning Paradigms

The book "Algorithms in Machine Learning Paradigms" offers a comprehensive examination of algorithms within various paradigms of machine learning. It addresses a wide range of learning problems and their applications in different fields. The authors, including Somnath Mukhopadhyay, Kousik Dasgupta, Jyotsna Kumar Mandal, and Paramartha Dutta, analyze concepts such as prediction, conceptual learning, explanation-based learning, as well as exemplar and statistical rule-based learning. Additionally, innovative approaches such as quantum-inspired learning, optimization-based learning, and hybrid intelligence are discussed. This academic book is aimed at professionals and students seeking a deeper understanding of algorithms in machine learning.