The field of Statistics is constantly challenged by the problems that science and industry brings to its door. In the early days, these problems often came from agricultural and industrial experiments and were relatively small in scope. With the advent of computers and the information age, statistical problems have exploded both in size and complexity. Challenges in the areas of data storage, organization and searching have led to the new field of “data mining”; statistical and computational problems in biology and medicine have created “bioinformatics.” Vast amounts of data are being generated in many fields, and the statistician’s job is to make sense of it all: to extract important patterns and trends, and understand “what the data says.” We call this learning from data.
The Elements of Statistical Learning
The book unifies key machine learning and statistical methods — from linear models and decision trees to boosting, support vector machines, and unsupervised learning. Its clear explanations, mathematical rigor, and practical examples have made it a cornerstone for researchers and practitioners alike. The book has deeply influenced both statistics and computer science, shaping how modern data science integrates theory with application, and remains a must-read reference for anyone serious about statistical learning and machine learning.