Introduction To Statistical Learning 2nd Edition Pdf

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Spencer Prather

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Aug 5, 2024, 6:26:35 AM8/5/24

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AnIntroduction 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, deep learning, survival analysis, multiple testing, 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.

This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of nave Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book." (Larry Wasserman, Professor, Department of Statistics and Machine Learning Department, Carnegie Mellon University)

Gareth James is a professor of data sciences and operations, and the E. Morgan Stanley Chair in Business Administration, at the University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.

Daniela Witten is a professor of statistics and biostatistics, and the Dorothy Gilford Endowed Chair, at the University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, with an emphasis on unsupervised learning.

Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.

Hi guys! I recently decided to refresh a bit some of my university learnings on statistics and have been looking for good books. Unfortunately those those that I was learning from weren't especially practice orientated - I'm looking for ones that talk about stats from a more data science, practical point of view and blend nicely some of the statistical concepts with machine learning. Would you have anything good to recommend? Thank!

I completely agree: Applied Predictive Modelling is definitely my number 1. I read "An Introduction to Statistical Learning" and use "Elements" more as a go to reference when I need to check something cause that one is really a biggie. I completely agree that all 3 are really awesome but more for ML purposes, whereas I'm looking for something slightly more pure stats orientated. Describing things such as: estimators theory, hypothesis testing, confidence intervals, power and sample size etc. Anything else you can recommend?

I'm a big fan of Richard McElreath's Statistical Rethinking. It provides a great intro to Bayesian statistical applications, with lots of practice problems. No dedicated section on machine learning, though.

I second Richard McElreath's Statistical Rethinking. I also like Andrew Gelman and Jennifer Hill's Data Analysis using Regression and Multilevel/Hierarchical Models. I believe Gelman is working on a second edition that uses Stan/RStan. No machine learning though.

+1 for Mostly Harmless Econometrics. IMHO one of the most underappreciated gems in this genre. Econometrics hasn't reached the same buzzword status as machine learning or data science, but it brings a really valuable perspective thinking "what would be my ideal data and experimental design for this?" as a tool to think about how to approach a problem with whatever actual data and information you have

Highly recommend Statistics by Freedman, Pisani and Purves as a first statistics text. Clearest and easiest to read math book I've ever found. Mathematical details at about a highschool level, and it really does wonders for intuition. Also it's great to share with family members.

My recommendation might be a bit off from what you're asking for but I loved Naked Statistics by Charles Wheelan because reading it was the first time I felt like I really would be able to learn statistics and helped give me the motivation/courage to crack more intimidating textbooks to get into the details.

In addition to the classics, of Introduction to Statistical Learning in R and Elements of Statistical Learning, I also recommend the newer entry from Hastie, Computer Age Statistical Inference. I haven't finished CASI -- only read a few random chapters -- but I really like how it is laid out, with focus on not just the math, but also the history. It's a great way to introduce some of the statistics in data science and help explain how the field has grown into what it is today.

If you are not 100% focused on using R and open to learning through Python, I also highly recommend the Allen Downey books Think Stats 2 and Think Bayes. They are well written and favor teaching through code instead of just math, which was really helpful for me.

Lastly, I thoroughly enjoyed Machine Learning for Hackers and its corresponding GitHub repo. It's a whirlwind tour of the most common/basic algorithms used in data science (outside of deep learning) and is focused more on making sure you understand the high-level concepts and how to use them than making sure you understand the math. In that regard, it's a great companion book to ISLR/ESL.

Thank you so much guys! I think everyone in this group will find the best content for his/ herself. Personally I will start either with this one: "All of Statistics: A Concise Course in Statistical Inference" or "Statistical Rethinking: A Bayesian Course with Examples in R and Stan" just to get all the foundations right. I think " Statistics by Freedman, Pisani and Purves" would be a good choice but it has twice as many pages so I'd rather take a shortcut here

If you're ever stuck and need something explained in a new way, I really like Introduction to Probability Theory and Statistics by Javier R Movellan.

Among other things, it's free and online ! I often use it as my go-to for explaining something that might require a quick stats refresher.

An Introduction to Statistical Learning, with Applications in R, written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, is an absolute classic in the space. The book, a staple of statistical learning texts, is accessible to readers of all levels, and can be read without much of an existing foundational knowledge in the area.

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

An Introduction to Statistical Learning, with Applications in R (ISLR) can be considered a less advanced treatment of the topics found in another classic of the genre written by some of the same authors, The Elements of Statistical Learning. Another major difference between these 2 titles, beyond the level of depth of the material covered, is that ISLR introduces these topics alongside practical implementations in a programming language, in this case R.

As mentioned above, the book is an absolute classic in the genre. But you don't need to take my word for how essential of a text it is. Here's a review (taken from the book's Amazon site) by Larry Wasserman of Carnegie Mellon University:

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