Contents
What should I read before the elements of statistical learning?
Most Recommended Books
- #1 The Elements of Statistical Learning: Data Mining, Inference and Prediction (“ESL”)
- #2 Pattern Recognition and Machine Learning (“PRML”)
- #3 Machine Learning: A Probabilistic Perspective (“MLAPP”)
- #4 Deep Learning.
- #5 An Introduction to Statistical Learning with Applications in R (“ISLR”)
Is Elements of Statistical Learning a good book?
Very comprehensive, sufficiently technical to get most of the plumbing behind machine learning. 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).
What are the elements of statistics?
Descriptive analysis and treatment of data, probability and probability distributions, statistical inferences, linear regression and correlations, chi-square, and some nonparametric statistics. Preexisting statistical computer programs may be used for some applications.
What is the function of statistical learning?
Statistical learning theory is a framework for machine learning, drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the problem of finding a predictive function based on data. The goal of learning is prediction.
What are statistical learning methods?
Statistical Learning is a set of tools for understanding data. These tools broadly come under two classes: supervised learning & unsupervised learning. Generally, supervised learning refers to predicting or estimating an output based on one or more inputs.
What are the 3 components of statistics?
Sampling: – Decide how to select the sample. – Collect the sample data. Analysis: – Describe the sample data, using graphs and numerical summaries. – Compute measures of uncertainty to project the sample summaries to the whole population.
Is Machine Learning a statistical method?
Machine learning is based on statistical learning theory, which is still based on this axiomatic notion of probability spaces. This theory was developed in the 1960s and expands upon traditional statistics.
Is there an online version of the introduction to statistical learning?
A free online version of An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013) is available from that website. Springer has agreed to this, so no need to worry about copyright. Of course you may not distribiute printed versions of this pdf file.
Which is the best book for statistical learning?
The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website.
Where can I get statistical learning for free?
You get R for free from http://cran.us.r-project.org/. Typically it installs with a click. You get RStudio from http://www.rstudio.com/ , also for free, and a similarly easy install. It will take approximately 3-5 hours per week to go through the materials and exercises in each section.
What are some unsupervised methods for statistical learning?
Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics.