The elements of statistical learning: data mining, inference, and prediction

he Elements Of Statistical Learning is an authoritative guide on the applications of statistics in various fields of study such as medicine, biology, finance and marketing. Summary of the Book With rapidly expanding computation and information technology in the last decade, there have been vast amount of data in various fields of study. The challenge of understanding these data has led to the development of new tools in the field of statistics which has led to coming up of new areas such as data mining, machine learning, and bioinformatics. The book is packed with examples and illustrations which makes it easy for the readers to understand the complex concepts of statistics easily. The topics covered in the book include neural networks, support vector machines, classification trees, and boosting. This book is essential for statisticians and analysts. About the Authors Trevor Hastie is an American statistician and computer scientist. He is currently a Professor in the Department of Statistics at Stanford University. Robert Tibshirani is a Professor in the Departments of Statistics and Health Research and Policy at Stanford University. Jerome Isaac Friedman is an American physicist and a Professor of Physics at the Massachusetts Institute of Technology. He is a 1990 Physics Nobel Laureate for working on the internal structure of protons.