The Elements of Statistical Learning

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework.

Book Description

Topics included: Overview of Supervised Learning • Linear Methods for Regression • Linear Methods for Classification • Basis Expansions and Regularization • Kernel Smoothing Methods • Model Assessment and Selection • Model Inference and Averaging • Additive Models, Trees, and Related Methods • Boosting and Additive Trees • Neural Networks • Support Vector Machines and Flexible Discriminants • Prototype Methods and Nearest-Neighbors • Unsupervised Learning • Random Forests • Ensemble Learning • Undirected Graphical Models • High-Dimensional Problems.

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