# An Introduction to Statistical Learning

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- Author : Gareth James
- Publisher : Springer Science & Business Media
- Release : 24 June 2013
- ISBN : 9781461471387
- Page : 426 pages
- Rating : 5/5 from 1 voters

## Download An Introduction to Statistical Learning in PDF, Epub and Kindle

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 some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

### An Introduction to Statistical Learning

- Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
- Publisher : Springer Science & Business Media
- Release Date : 2013-06-24
- ISBN : 9781461471387

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 some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.

### Introduction to Statistical Machine Learning

- Author : Masashi Sugiyama
- Publisher : Morgan Kaufmann Publishers
- Release Date : 2015-10-12
- ISBN : 0128021217

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range

### Introduction to Statistical Machine Learning

- Author : Masashi Sugiyama
- Publisher : Morgan Kaufmann
- Release Date : 2015-10-31
- ISBN : 9780128023501

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range

### Introduction to Statistical and Machine Learning Methods for Data Science

- Author : Carlos Andre Reis Pinheiro,Mike Patetta
- Publisher : SAS Institute
- Release Date : 2021-08-06
- ISBN : 9781953329622

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book

### Statistical Machine Learning

- Author : Richard Golden
- Publisher : CRC Press
- Release Date : 2020-06-24
- ISBN : 9781351051491

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine

### Introduction to Statistical Relational Learning

- Author : Lise Getoor,Ben Taskar
- Publisher : MIT Press
- Release Date : 2019-09-22
- ISBN : 9780262538688

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging

### Machine Learning

- Author : Steven W. Knox
- Publisher : John Wiley & Sons
- Release Date : 2018-03-08
- ISBN : 9781119439073

AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding

### Machine Learning

- Author : RODRIGO F MELLO,Moacir Antonelli Ponti
- Publisher : Springer
- Release Date : 2018-08-01
- ISBN : 9783319949895

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. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with

### Statistical Reinforcement Learning

- Author : Masashi Sugiyama
- Publisher : CRC Press
- Release Date : 2015-03-16
- ISBN : 9781439856901

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amo

### Neural Networks and Statistical Learning

- Author : Ke-Lin Du,M. N. S. Swamy
- Publisher : Springer Nature
- Release Date : 2019-09-12
- ISBN : 9781447174523

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 working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding,

### Machine Learning and Data Science

- Author : Daniel D. Gutierrez
- Publisher : Technics Publications
- Release Date : 2015-11-01
- ISBN : 9781634620987

A practitioner’s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set

### The Elements of Statistical Learning

- Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
- Publisher : Springer Science & Business Media
- Release Date : 2013-11-11
- ISBN : 9780387216065

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

### An Elementary Introduction to Statistical Learning Theory

- Author : Sanjeev Kulkarni,Gilbert Harman
- Publisher : John Wiley & Sons
- Release Date : 2011-06-09
- ISBN : 1118023463

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors

### Introduction to Probability

- Author : Joseph K. Blitzstein,Jessica Hwang
- Publisher : CRC Press
- Release Date : 2014-07-24
- ISBN : 9781498759762

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

### Mathematics for Machine Learning

- Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
- Publisher : Cambridge University Press
- Release Date : 2020-04-23
- ISBN : 9781108470049

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.