Stanford classic textbook 《The Element of Statistical Learning》（ abbreviation ESL） Statistical learning known as the frequency school 「 Bible 」, from Trevor Hastie、Robert Tibshirani、Jerome Friedman The three masters worked together to complete . This book introduces
neural network 、
Support vector machine 、 Classification tree and boosting、 Graph model 、 Random forest, etc
machine learning Algorithm , It can help readers understand
machine learning Algorithm overview .
But because this book involves a lot of formulas 、 Matrix derivation and many other difficult contents , It is more suitable for individuals with advanced training in Mathematical Science , And for beginners , It's difficult to learn , therefore ,Trevor Hastie They wrote another entry-level book 《Introduction to Statistical Learning with R（ Introduction to statistical learning ： be based on R application ）》（ISL）, Help more people get started as soon as possible .
ISL Weakens the details of mathematical derivation , Pay more attention to the application of methods , amount to ESL Guide to reading , Very popular among entry-level readers . Each chapter contains an experiment , use R Language implementation . These experiments provide readers with valuable practical experience .
at present , The book has been translated into many languages , Including Chinese 、 Italian 、 Japanese 、 Korean 、 Russian and Vietnamese .
Now? ,ISL Updated to the second edition （ISLRv2）, Compared to the first edition , The second edition adds
Deep learning （ The first 10 Chapter ）、 Survival analysis （ The first 11 Chapter ）、 Multiple tests （ The first 13 Chapter ）.ISLRv2 Some chapters of the first edition have also been greatly expanded ：
Naive Bayes And generalized linear model （ The first 4 Chapter ）, Bayesian additivity
Back to the tree （ The first 8 Chapter ）, Matrix completion （ The first 12 Chapter ）.
Besides ,ISLRv2 The whole... Has been updated R Code experiments .
Book address ：https://www.statlearning.com/
Netizens strongly recommend ： very nice , This is a great introductory book .
There are as many books as 597 page , common 13 Chapters , It mainly includes ：
Statistical learning ;
Linear regression ;
Resampling Method ;
linear Model selection And Regularization ;
Nonlinear models ;
Tree based approach ;
Support vector machine ;
Deep learning ;
Survival analysis and deletion data ;
Unsupervised learning ;
Multiple tests .
To be specific , This book 2 This chapter introduces the basic terms and concepts behind statistical learning , In addition, it introduces k - Nearest neighbor classifier , This is a very simple method , Very effective in dealing with many problems .
The first 3 Zhang He 4 This chapter introduces the classical linear methods for regression and classification . In particular , The first 3 Chapter reviews
Linear regression , This is the basic starting point of all regression methods ; The first 4 Chapter discusses two of the most important classical classification methods , Logistic regression and linear discriminant analysis .
A core problem in all statistical learning situations is to choose the best method for a given application . therefore , The first 5 This chapter introduces cross validation and bootstrap, They can be used to estimate the accuracy of many different methods to select the best method .
The first 6 Many linear methods are considered in this chapter , Including classical and more modern linear methods , They provide a reference to the standard
Linear regression Potential improvements to , Including ridge regression 、 Principal component regression and Lasso etc. .
The remaining chapters are mainly nonlinear statistical learning . The first 7 First, some nonlinear methods are introduced , These methods can solve the problem of only one input variable , Then it shows how to use these methods to fit a nonlinear additive model with multiple inputs .
The first 8 In this chapter, the tree based method is studied , Include bagging、boosting And random forest .
The first 9 Describes the
Support vector machine The content such as .
The first 10 Describes the
Deep learning , This is a kind of non
Linear regression And the method of classification , In recent years, it has received wide attention .
The first 11 This chapter discusses survival analysis , This is a regression method , It is specially used when the output variable is deleted , That is, incomplete observation .
The first 12 This chapter introduces the unsupervised setup , There are input variables in the unsupervised setting , But no output variables . Specially , Principal component analysis is proposed 、k - Mean clustering and hierarchical clustering . Last , The first 13 This chapter discusses the very important topic of multiple hypothesis testing .
The authors introduce
The authors of the books are... From left to right ：Gareth James、Daniela Witten、Trevor Hastie and Rob Tibshirani.
Gareth James Is the associate dean of the Marshall School of business at the University of Southern California , The main research fields include functional data analysis 、 High dimensional regression 、 Statistical problems in marketing .
Daniela Witten Is an American biostatistician , The main research area is how to use
machine learning To understand high-dimensional data .
Trevor Hastie I'm a professor at Stanford University , It used to be AT&T A technician at Bell Labs .2018 year ,Hastie He was elected to the National Academy of Sciences . His main research field is applied statistics .
Rob Tibshirani He is a professor in the Department of statistical and biomedical data science at Stanford University , He has developed statistical tools for analyzing complex data sets .
Book catalogue ：