The Elements of Statistical Learning

Download or Read online The Elements of Statistical Learning full in PDF, ePub and kindle. This book written by Trevor Hastie and published by Springer Science & Business Media which was released on 09 August 2022 with total pages 533. Read The Elements of Statistical Learning Book directly on your devices, fast download and no annoying ads.

The Elements of Statistical Learning

The Elements of Statistical Learning

Author	: Trevor Hastie
Publisher	: Springer Science & Business Media
Release Date	: 09 August 2022
ISBN	: 0387952845
Pages	: 533 pages
Rating	: 4.5/5 (14 users)

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Download or Read Online The Elements of Statistical Learning in PDF, Epub and Kindle

This book describes the important ideas 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 should be a valuable resource for statisticians and anyone interested in data mining in science or industry.

The Elements of Statistical Learning

The Elements of Statistical Learning by Trevor Hastie,Robert Tibshirani,Jerome H. Friedman

This book describes the important ideas 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 should be a valuable resource for statisticians and anyone interested in data mining in

An Introduction to Statistical Learning

An Introduction to Statistical Learning by Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani

An Introduction to Statistical Learning 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. This book presents

The Elements of Statistical Learning

The Elements of Statistical Learning by Trevor Hastie,Robert Tibshirani,Jerome H. Friedman

Download or read online The Elements of Statistical Learning written by Trevor Hastie,Robert Tibshirani,Jerome H. Friedman, published by Unknown which was released on 2009. Get The Elements of Statistical Learning Books now! Available in PDF, ePub and Kindle.

Additive Logistic Regression

Additive Logistic Regression by Jerome H. Friedman,Trevor Hastie,Robert Tibshirani

Download or read online Additive Logistic Regression written by Jerome H. Friedman,Trevor Hastie,Robert Tibshirani, published by Unknown which was released on 1998. Get Additive Logistic Regression Books now! Available in PDF, ePub and Kindle.

Statistical Learning with Sparsity

Statistical Learning with Sparsity by Trevor Hastie,Robert Tibshirani,Martin Wainwright

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help

The Nature of Statistical Learning Theory

The Nature of Statistical Learning Theory by Vladimir Vapnik

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning

Statistical Learning with Math and Python

Statistical Learning with Math and Python by Joe Suzuki

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction

Deep Learning

Deep Learning by Ian Goodfellow,Yoshua Bengio,Aaron Courville

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO

Think Stats

Think Stats by Allen B. Downey

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly

All of Statistics

All of Statistics by Larry Wasserman

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It

Neural Networks and Statistical Learning

Neural Networks and Statistical Learning by Ke-Lin Du,M. N. S. Swamy

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical

Multivariate Statistical Machine Learning Methods for Genomic Prediction

Multivariate Statistical Machine Learning Methods for Genomic Prediction by Osval Antonio Montesinos López,Abelardo Montesinos López,José Crossa

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics

Machine Learning

Machine Learning by RODRIGO F MELLO,Moacir Antonelli Ponti

This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning.

Introduction to Probability

Introduction to Probability by Joseph K. Blitzstein,Jessica Hwang

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional

Computer Age Statistical Inference

Computer Age Statistical Inference by Bradley Efron,Trevor Hastie

Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders.