
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
|
|
- Title The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
- Author(s) Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Publisher: Springer; 2nd edition (2016); eBook (Online Corrected 12th printing - Jan 13, 2017)
- Hardcover 745 pages
- eBook PDF (764 pages)
- Language: English
- ISBN-10: 0387848576
- ISBN-13: 978-0387848570
- Share This:
![]() |
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. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting - the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
About the Authors-
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
- Machine Learning
- Statistics and SAS Programming
- Data Analysis and Data Mining
- Probability, Stochastic Process, Queueing Theory, etc.

- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
- The Mirror Site (1) - PDF
-
An Introduction to Statistical Learning (Gareth James, et al)
It provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
-
Probabilistic Machine Learning: An Introduction (Kevin Murphy)
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.
-
Statistical Learning and Sequential Prediction
This book focuses on theoretical aspects of Statistical Learning and Sequential Prediction, a unified approach to analyzing learning in both scenarios, brings together ideas from probability and statistics, game theory, algorithms, and optimization.
-
Statistical Foundations of Machine Learning: The Handbook
This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.
-
Bayesian Methods for Hackers: Probabilistic Programming
This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation - no advanced mathematics required.
-
Pattern Recognition and Machine Learning (Christopher Bishop)
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.
-
Foundations of Machine Learning (Mehryar Mohri, et al)
This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.
-
The Hundred-Page Machine Learning Book (Andriy Burkov)
Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.
:
|
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |