Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of
The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies
Wan and Mak describe machine-learning approaches to determining automatically the location of a protein within a cell in order to facilitate drug discovery and drug design. The approaches exploit the
This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and n
Data mining is an increasingly valuable tool within the institution of higher education. Educational Data Mining (EDM) has emerged as a field in its own right, focusing on data mining techniques
"Clearly written and well organized, this book shows how to apply the principles of universal design for learning (UDL) across all subject areas and grade levels. The editors and contributors describe
Complex multivariate testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. As a result, modern statistics needs permutation
A timely overview of all flow and transport processes in which chemical or physico-chemical phenomena are essential, bringing together previously scattered theoretical and experimental results necessa
