Contents
Which is an alternative to elements of statistical learning?
I’ve heard that the classic text on Linear Models by Searle is amazing, but have not had time to read it. For alternatives to Elements of Statistical Learning, my #1 choice by far are the texts by Theodoridis, namely Machine Learning, and Pattern Recognition.
Which is the best book for statistical learning?
Elements of Statistical Learning: data mining, inference and prediction (2nd Edition) (with J. Friedman, Springer-Verlag, 2009). The lectures will consist of high-quality projected presentations and discussion.
Which is the best book for data mining?
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.
How are new tools used in the field of Statistics?
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.
Is the elements of statistical learning a reference book?
Very useful as a reference book (actually, there is no other complete reference book). The authors are the real thing (Tibshirani is the one behind the LASSO regularization technique). Uses some mathematical statistics without the burdens of measure theory and avoids the obvious but complicated proofs.
Where can I download the Stanford Statistical Learning book?
Download the free pdf from the Stanford site. If you find a companion text, only then buy the hard copy.
Who are the professors of Statistics at Stanford?
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.