--- product_id: 51474059 title: "The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)" price: "1090 kr" currency: DKK in_stock: true reviews_count: 13 url: https://www.desertcart.dk/products/51474059-the-elements-of-statistical-learning-data-mining-inference-and-prediction store_origin: DK region: Denmark --- # The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) **Price:** 1090 kr **Availability:** ✅ In Stock ## Quick Answers - **What is this?** The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) - **How much does it cost?** 1090 kr with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.dk](https://www.desertcart.dk/products/51474059-the-elements-of-statistical-learning-data-mining-inference-and-prediction) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description Buy The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) Second Edition 2009 by Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome (ISBN: 9780387848570) from desertcart's Book Store. Everyday low prices and free delivery on eligible orders. Review: Great book for those doing a PhD in Stochastic Optimization or ML - This book has been an excellent read throughout my PhD and have relied on it heavily. Review: Relevant, Well Structured and Digestible - Some context first: I'm studying my fourth year in a computer engineering program, having studied lightweight mathematics courses only, which is basically calculus, linear algebra, discrete mathematics and matematical statistics. Our machine learning course has two recommended literatures of which "The Elements of Statistical Learning" (ESL) was one of them, while the primary was Pattern Recognition and Machine Learning (PRML). My experience with the book so far if very positive. It contains incredibly relevant machine learning methods/tools which many other books, most notably PRML, doesn't touch upon or at least explain very shortly, which are extensively used in practice. Most notably: Support Vector Machines, Random Forests and Ensemble Learning. Also, the structure of ESL has made a lot more sense to me compared to PRML, it wraps parts of the field into more easily digestible chunks, and therefore makes for a better reference than PRML (just compare the table of contents). Also, as the authors themselves point out, the book itself will rather the reader understands the intuition, algorithm and the cases in which they perform good/bad rather than the mathematical background/proofs behind them (don't worry, most of them are still presented in ESL though). In conclusion, if you can accept the skimming of proof and some rigour in ESL, this book is perfect, and summarizes a large part of the field in such a way that even a mathematically mediocre computer scientist is able to somewhat grasp and apply in real world problems. However, if you want to get the entire picture, you might want to read both ESL and PRML, which will give you some of that Bayesian goodies as well. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | 126,946 in Books ( See Top 100 in Books ) 22 in Data Mining (Books) 81 in Experiments, Instruments & Measurements 101 in Applied Mathematics (Books) | | Customer reviews | 4.6 4.6 out of 5 stars (1,289) | | Dimensions | 23.62 x 15.24 x 3.56 cm | | Edition | Second Edition 2009 | | ISBN-10 | 0387848576 | | ISBN-13 | 978-0387848570 | | Item weight | 1.41 kg | | Language | English | | Print length | 767 pages | | Publication date | 9 Feb. 2009 | | Publisher | Springer | | Reading age | 10 years and up | ## Images ![The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) - Image 1](https://m.media-amazon.com/images/I/517TrzchOML.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Great book for those doing a PhD in Stochastic Optimization or ML *by A***L on 1 December 2025* This book has been an excellent read throughout my PhD and have relied on it heavily. ### ⭐⭐⭐⭐⭐ Relevant, Well Structured and Digestible *by A***R on 24 November 2016* Some context first: I'm studying my fourth year in a computer engineering program, having studied lightweight mathematics courses only, which is basically calculus, linear algebra, discrete mathematics and matematical statistics. Our machine learning course has two recommended literatures of which "The Elements of Statistical Learning" (ESL) was one of them, while the primary was Pattern Recognition and Machine Learning (PRML). My experience with the book so far if very positive. It contains incredibly relevant machine learning methods/tools which many other books, most notably PRML, doesn't touch upon or at least explain very shortly, which are extensively used in practice. Most notably: Support Vector Machines, Random Forests and Ensemble Learning. Also, the structure of ESL has made a lot more sense to me compared to PRML, it wraps parts of the field into more easily digestible chunks, and therefore makes for a better reference than PRML (just compare the table of contents). Also, as the authors themselves point out, the book itself will rather the reader understands the intuition, algorithm and the cases in which they perform good/bad rather than the mathematical background/proofs behind them (don't worry, most of them are still presented in ESL though). In conclusion, if you can accept the skimming of proof and some rigour in ESL, this book is perfect, and summarizes a large part of the field in such a way that even a mathematically mediocre computer scientist is able to somewhat grasp and apply in real world problems. However, if you want to get the entire picture, you might want to read both ESL and PRML, which will give you some of that Bayesian goodies as well. ### ⭐⭐⭐⭐⭐ The ML Bible *by B***Y on 29 July 2014* Having completed the Coursera Stanford Machine Learning course I wanted to know more and this came up at the top recommended book in Amazon for ML. I downloaded the free PDF but it's huge and I find it impossible to read a PDF on a screen so I forked out for the hardback paper copy. I have to say this is well worth it, incredible scope of coverage and the colouring makes it more easy to understand (none of this stuff is actually 'easy'). This IMO is genuinely THE bible for Machine Learning. ## Frequently Bought Together - The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) - An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics) - Deep Learning (Adaptive Computation and Machine Learning series) --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.dk/products/51474059-the-elements-of-statistical-learning-data-mining-inference-and-prediction](https://www.desertcart.dk/products/51474059-the-elements-of-statistical-learning-data-mining-inference-and-prediction) --- *Product available on Desertcart Denmark* *Store origin: DK* *Last updated: 2026-04-23*