Facebook Twitter Pinterest
EUR 55,99
  • Statt: EUR 61,00
  • Sie sparen: EUR 5,01 (8%)
  • Alle Preisangaben inkl. MwSt.
Nur noch 20 auf Lager (mehr ist unterwegs).
Verkauf und Versand durch Amazon. Geschenkverpackung verfügbar.
Menge:1
An Introduction to Statis... ist in Ihrem Einkaufwagen hinzugefügt worden
Möchten Sie verkaufen?
Zur Rückseite klappen Zur Vorderseite klappen
Hörprobe Wird gespielt... Angehalten   Sie hören eine Hörprobe des Audible Hörbuch-Downloads.
Mehr erfahren
Alle 3 Bilder anzeigen

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) (Englisch) Gebundene Ausgabe – 11. Januar 2016

5.0 von 5 Sternen 3 Kundenrezensionen

Alle Formate und Ausgaben anzeigen Andere Formate und Ausgaben ausblenden
Preis
Neu ab Gebraucht ab
Gebundene Ausgabe
"Bitte wiederholen"
EUR 55,99
EUR 49,82 EUR 49,81
89 neu ab EUR 49,82 15 gebraucht ab EUR 49,81
click to open popover

Wird oft zusammen gekauft

  • An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
  • +
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
  • +
  • Applied Predictive Modeling
Gesamtpreis: EUR 192,97
Die ausgewählten Artikel zusammen kaufen

Es wird kein Kindle Gerät benötigt. Laden Sie eine der kostenlosen Kindle Apps herunter und beginnen Sie, Kindle-Bücher auf Ihrem Smartphone, Tablet und Computer zu lesen.

  • Apple
  • Android
  • Windows Phone

Geben Sie Ihre Mobiltelefonnummer ein, um die kostenfreie App zu beziehen.

Jeder kann Kindle Bücher lesen — selbst ohne ein Kindle-Gerät — mit der KOSTENFREIEN Kindle App für Smartphones, Tablets und Computer.



Produktinformation

Produktbeschreibungen

Pressestimmen

From the book reviews:

“This book has a very strong advantage that sets it well ahead of the competition when it comes to learning about machine learning: it covers all of the necessary details that one has to know in order to apply or implement a machine learning algorithm in a real-world problem. Hence, this book will definitely be of interest to readers from many fields, ranging from computer science to business administration and marketing.” (Charalambos Poullis, Computing Reviews, September, 2014)

“The book provides a good introduction to R. The code for all the statistical methods introduced in the book is carefully explained. … the book will certainly be useful to many people (including me). I will surely use many examples, labs and datasets from this book in my own lectures.” (Pierre Alquier, Mathematical Reviews, July, 2014)

“The stated purpose of this book is to facilitate the transition of statistical learning to mainstream. … it adds information by including more detail and R code to some of the topics in Elements of Statistical Learning. … I am having a lot of fun playing with the code that goes with book. I am glad that this was written.” (Mary Anne, Cats and Dogs with Data, maryannedata.com, June, 2014)

“This book (ISL) is a great Master’s level introduction to statistical learning: statistics for complex datasets. … the homework problems in ISL are at a Master’s level for students who want to learn how to use statistical learning methods to analyze data. … ISL contains 12 very valuable R labs that show how to use many of the statistical learning methods with the R package ISLR … .” (David Olive, Technometrics, Vol. 56 (2), May, 2014)

“Written by four experts of the field, this book offers an excellent entry to statistical learning to a broad audience, including those without strong background in mathematics. … The end-of-chapter exercises make the book an ideal text for

both classroom learning and self-study. … The book is suitable for anyone interested in using statistical learning tools to analyze data. It can be used as a textbook for advanced undergraduate and master’s students in statistics or related quantitative fields.” (Jianhua Z. Huang, Journal of Agricultural, Biological, and Environmental Statistics, Vol. 19, 2014)

“It aims to introduce modern statistical learning methods to students, researchers and practitioners who are primarily interested in analysing data and want to be confined only with the implementation of the statistical methodology and subsequent interpretation of the results. … the book also demonstrates how to apply these methods using various R packages by providing detailed worked examples using interesting real data applications.” (Klaus Nordhausen, International Statistical Review, Vol. 82 (1), 2014)

“The book is structured in ten chapters covering tools for modeling and mining of complex real life data sets. … The style is suitable for undergraduates and researchers … and the understanding of concepts is facilitated by the exercises, both practical and theoretical, which accompany every chapter.” (Irina Ioana Mohorianu, zbMATH, Vol. 1281, 2014) 

"The book excels in providing the theoretical and mathematical basis for machine learning, and now at long last, a practical view with the inclusion of R programming examples. It is the latter portion of the update that I’ve been waiting for as it directly applies to my work in data science. Give the new state of this book, I’d classify it as the authoritative text for any machine learning practitioner...This is one book you need to get if you’re serious about this growing field." (Daniel Gutierrez, Inside Big Data, inside-bigdata.com, October 2013)

Rezension

"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)

Alle Produktbeschreibungen

Welche anderen Artikel kaufen Kunden, nachdem sie diesen Artikel angesehen haben?

Kundenrezensionen

5.0 von 5 Sternen
5 Sterne
3
4 Sterne
0
3 Sterne
0
2 Sterne
0
1 Stern
0
Alle 3 Kundenrezensionen anzeigen
Sagen Sie Ihre Meinung zu diesem Artikel

Top-Kundenrezensionen

Format: Gebundene Ausgabe Verifizierter Kauf
Wie schon hier erwähnt, ist An Introduction to Statistical Learning (ISL) eine ausgezeichnete Einführung ins Machine Learning. Man kann es als den kleinen Bruder von "The Elements of Statistical Lernen" (ESL) sehen. Es werden alle relevanten Themen vom Statistical Learning/Machine Learning (classification, clustering, supervised, unsupervised, usw.) in wenigen Seiten behandelt. Denn ISL ist extrem gut erklärt und benutzt eine einfache Sprache. Wenn man noch zusätzlich das Stanford MOOC "Statistical Learning" belegt, bekommt man eine sehr fundierte Basis.

ISL verzichtet auf komplexe mathematische Beweise und ist tatsächlich als Anwendungsbuch zum Selbstlernen gedacht. Es wird keine großen Vorkenntnissen erwartet. Man lernt anhand von der Programmiersprache R, wie man Datensätze analysiert und Zusammenhänge vorhersagen kann. Wenn man noch über die Theorie dahinter erfahren möchte, oder tiefer ins Thema gehen will, ist ESL bestens empfohlen. Hier machen die Autoren sehr deutlich, dass ISL sich um ein Praxisbuch handelt.

Das Buch ist auch sehr schön und hochwertig gemacht (man merkt es an den Preis). Die Seiten sind bunt und aus qualitativem Papier. Der Preis ist außerdem komplett gerechtfertigt, da es einem sofort klar wird, wie viel Zeit und Leidenschaft die Autoren investiert haben, um ein konzises aber präzises Fachbuch zu konzipieren. Es handelt sich um ein extrem didaktisches Buch und leider ist dies in der Informatik/Mathematik häufig eine Seltenheit.

Man kann das Buch kostenlos (und legal) im Internet als PDF herunterladen. Die Autoren haben es zur Verfügung gestellt. Es lohnt sich aber zum Kauf. Denn die Autoren haben es sehr wohl verdient.

ISL wird wie ihr Großbruder zum Standardwerk und es kann jedem empfohlen werden, der ein Interesse an dem Thema hat.
Kommentar 10 Personen fanden diese Informationen hilfreich. War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Missbrauch melden
Format: Gebundene Ausgabe Verifizierter Kauf
Ich habe dieses Buch erworben, während ich parallel an dem freien Stanford-Kurs zu demselben Thema teilgenommen habe.
Ich kann beides, den Kurs und dieses Buch, sehr empfehlen. Sehr didaktisch und gut lesbar aufgebaut. Die Beispiele in R sind hilfreich und ebenfalls gut nachvollziehbar.
Gewisse Grundkenntnisse in Mathematik, insbesondere Statistik und Lineare Algebra, sollte man mitbringen (so auf dem Level 1.-2. Semester eines Ingenieurstudiums). Mehr Voraussetzungen sind aus meiner Sicht nicht nötig.
Das Buch, wenngleich etwas teuer, hat mir Freude bereitet und mich motiviert, mich weiter in dem Thema Machine Learning zu vertiefen.
Das Buch ist übrigens im PDF-Format frei herunterladbar (siehe google).
Kommentar 3 Personen fanden diese Informationen hilfreich. War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Missbrauch melden
Format: Gebundene Ausgabe Verifizierter Kauf
What is so good about this book is not just the content (excellent explanations of all of the modern methods of statistical learning), but also the general quality of the presentation. Springer Verlag has made a great step forward in their most recent books. It has a very modern feel to it, the graphs are in color, and there are examples in R for each of the presented theoretical concepts. The authors describe their book as a precursor to the Elements of Statistical Learning, this book is much more approachable, and the wealth of examples make understanding each topic comparatively easy. Highly recommended for anybody wanting to actually implement statisticsl learning techniques.
Kommentar Eine Person fand diese Informationen hilfreich. War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Missbrauch melden

Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)

Amazon.com: HASH(0x98eb9f54) von 5 Sternen 119 Rezensionen
148 von 152 Kunden fanden die folgende Rezension hilfreich
HASH(0x98f12f30) von 5 Sternen wonderful but watch the movie 14. Februar 2014
Von I Teach Typing - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe Verifizierter Kauf
This is a wonderful book written by luminaries in the field. While it is not for casual consumption, it is a relatively approachable review of the state of the art for people who do not have the hardcore math needed for The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). This book is the text for the free Winter 2014 MOOC run out of Stanford called StatLearning (sorry Amazon will not allow me to include the website). Search for the class and you can watch Drs. Hastie and Tibshirani teach the material in this book.
99 von 102 Kunden fanden die folgende Rezension hilfreich
HASH(0x98e84990) von 5 Sternen Excellent Practical Introduction to Learning 24. Oktober 2013
Von Michael Tsiappoutas - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe Verifizierter Kauf
The book provides the right amount of theory and practice, unlike the earlier (venerable and, by now, stable) text authored (partly) by the last two authors of this one (Elements of Statistical Learning), which was/is a little heavy on the theoretical side (at least for practitioners without a strong mathematical background). The authors make no pretense about this either. The Preface says "But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics."

ISL is neither as comprehensive nor as in-depth as ESL. It is, however, an excellent introduction to Learning due to the ability of the authors to strike a perfect balance between theory and practice. Theory is there to aim the reader as to understand the purpose and the "R Labs" at the end of each chapter are as valuable (or perhaps even more) than the end-of-chapter exercises.

ISL is an excellent choice for a two-semester advanced undergraduate (or early graduate) course, practitioners trained in classical statistics who want to enter the Learning space, and seasoned Machine Learners. It is especially helpful for getting the fundamentals down without being bogged down in heavy mathematical theory, a great way to kick-off corporate Learning units, or as an aid to help statisticians and learners communicate better.

A needed and welcome addition to the Learning literature, authored by some of the most well respected names in industry and academia. A classic in the making. Recommended unreservedly.
____________________________________________
UPDATE (12/17/2013): Two of the authors (Hastie & Tibshirani) are offering a 10-week free online course (StatLearning: Statistical Learning) based on this book found at Stanford University's Web site (Starting Jan. 21, 2014). They also say that "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." Amazing opportunity! Enjoy!
____________________________________________
UPDATE (04/03/2014): I took the course above and found it very helpful and insightful. You don't need the course to understand the book. If anything, the course videos are less detailed than the book. It is certainly nice, though, to see the actual authors explain the material. Also, the interviews by Efron and Friedman were a nice touch. The course will be offered again in the future.
34 von 36 Kunden fanden die folgende Rezension hilfreich
HASH(0x98e84870) von 5 Sternen cover all of your bases 26. Januar 2014
Von Joseph Johnson - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe Verifizierter Kauf
If you want to build a comprehensive machine learning library, this would be the first book to purchase. While it does cover all of the basics, it is not watered down by any means. (I had the same fear as BK Reader) I found the following to be especially helpful;

1. Straight talk - These experts come right and say which methods work best under which circumstances. While there are many fancy algorithms covered in the book, they highlight the advantages of the simpler ones.
2. Emphasis on subjects that are not heavily addressed in most ML books - They thoroughly cover the challenges of high-dimensionality, data cleaning, and standardization. They do not limit their attention to these subjects to one chapter. They bring them up continually throughout the book.
3. Expertise - Dr. Hastie and Dr. Tibshirani are two of the thought leaders in statistical learning. You can be assured that you are learning from the best.
4. Many levels of depth - While the book does cover the basics, it is not watered down by any means. (I had the same worry as BK Reader) There is a great deal for any student of statistics; beginner or advanced.
5. R code - You are given enough code and examples to gain confidence in your ability to independently perform excellent analysis and modeling.
6. The concepts are just plain exciting! - You will feel an excitement as you discover and re-discover the algorithms they present.

The book is a standard work along with Elements of Statistical Learning and Pattern Recognition and Machine Learning (the Bayesian approach). If you enjoy the book, you may also want to consider Applied Predictive Modeling. It has the same style and approach.
22 von 23 Kunden fanden die folgende Rezension hilfreich
HASH(0x98e84960) von 5 Sternen Future Classic, hopefully? 22. Mai 2014
Von Joshua Davis - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe Verifizierter Kauf
This book was used in my graduate level Machine Learning class (with certain readings/problems from the authors other more challenging book, The Elements of Statistical Learning).

I loved the class and loved the book. I thought the applications with R made it far more accessible and made it easier to learn. While I totally love the theoretical underpinnings, sometimes they aren't the best to learn right away and applying the ideas make it easier to grasp.

Rob Tibs & Trevor Hastie also had an online course offered through Stanford's EdX that ran the same time I was taking the course. It had videos of Trevor and Rob explaining the concepts in the order they were presented in the book. The course also included exercises and quizzes. The best part of the online course was that Rob & Trevor were absolutely hilarious. I loved their commentary and their personalities clashed in the most humorous way possible; it is very easy to see that they love what they do and love each other's company.

I'd totally recommend this book. Keep an eye out for the next offering on Stanford's online course web page; it makes it a lot more enjoyable.
13 von 13 Kunden fanden die folgende Rezension hilfreich
HASH(0x98e8f888) von 5 Sternen A solid, well organized primer on Machine Learning in R 10. Januar 2014
Von Gary Montry - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
This book is probably not for experts (Springer has a more advanced book on Statistical Learning) but it's great for people who intermediate intermediate R users with a reasonable grasp of regression. The examples are easy to follow and the explanations are clear. This book is meant as a guide to IMPLEMENTING machine learning techniques in R. It does not cover the theory or the math behind the methods, nor does it offer proofs.

If you need a practical guide to implementing statistical learning in R, this is an excellent choice. Very understandable and accessible.
Waren diese Rezensionen hilfreich? Wir wollen von Ihnen hören.