The Elements of Statistical Learning

Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics. These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems. This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs.

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国外经典统计分析教材，讲述多变量统计的详细理论分析

Fundamentals of statistical signal processing: estimation theory的英文版，但是影印版，总共595页，清晰度还可以，希望能对需要的人有帮组

《Fundamentals of Statistical Signal Processing,Volume I & II》中文版

压缩文件中含有两个文件：《Statistical Digital Signal Processing and Modeling》一书的PDF文档和对应的Solution Maunal的PDF文件。欢迎兴趣的朋友下载。

Applied Linear Statistical Models By Kutner & Nachtsheim & Neter，哥伦比亚大学线性回归课程规定的教材，堪称经典，难度和深度兼具，想要了解线性回归模型朋友自取

The rise of probability theory changed that. Statistical inference compels us instead to rely on Fortuna as a servant of Minerva, to use chance and uncertainty to discover reliable knowledge. All flavors of statistical inference have this motivation. But Bayesian data analysis embraces it most fully, by using the language of chance to describe the plausibility of different possibilities. There are many ways to use the term “Bayesian.” But mainly it denotes a particular interpretation of probability. In modest terms, Bayesian inference is no more than counting the numbers of ways things can happen, according to our assumptions. Things that can happen more ways are more plausible. And since probability theory is just a calculus for counting, this means that we can use probability theory as a general way to represent plausibility, whether in reference to countable events in the world or rather theoretical constructs like parameters. Once you accept this gambit, the rest follows logically. Once we have defined our assumptions, Bayesian inference forces a purely logical way of processing that information to produce inference.

Yaser S. Abu-Mostafa 教授所著的机器学习教材下半部。 非影印，非常清晰。 是 caltech 《learning from data》和台大林轩田教授《机器学习基石》的指定教材。 主要讲授机器学习的理论问题。算是介绍学习理论比较浅显易懂的入门教材了。