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Free The Elements of Statistical Learning kindle õ eBook and Prediction ¹ feedmarkformulate

book The Elements of Statistical Learning

Free The Elements of Statistical Learning kindle õ eBook and Prediction ¹ feedmarkformulate ´ ➽ [Reading] ➿ The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) By Trevor Hastie ➲ ̵Pport vector machines classification trees and boosting the first comprehensive treatment of this topic in any book This major new edition features many topics not covered in the original including graphical models random forests ensemble methods least angle regression path algorithms for the lasso non negative matrix factorization and spectral clustering There is also a chapter on methods for ``wide'' data p bigger than n including multiple testing and false discovery rate 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 PRMLMy experience with the book so far if very positive It contains incredibly relevant machine learning methodstools 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 sense to me compared to PRML it wraps parts of the field into 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 goodbad rather than the mathematical backgroundproofs 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

mobi Ê Data Mining ´ Trevor Hastie

Inology 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 isa valuable resource for statisticians and anyone interested in data mining in science or industry The book's coverage is broad from supervised learning prediction to unsupervised learning The many topics include neural networks su Great book in terms of content basically my go to for learning data science but the cover came with no lacuer on it This is a really pedantic complaint and it really doesn't affect anything but it is rather annoying considering that every other textbook I have ordered from Springer had lacuered covers

Trevor Hastie ´ Inference and Prediction book

The Elements of Statistical Learning Data Mining Inference and Prediction Second Edition Springer Series in StatisticsDuring the past decade there has been an explosion in computation and information technology With it have come vast amounts of data in a variety of fields such as medicine biology finance and marketing 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 term Why this is not an ideal resource in digital format;I have a kindle Oasis I was surprised but excited when I saw this was available for it I was uickly very disappointed;The majority of the graphs have colored references Due to BW nature of e readers these will be lost to youThere is a great deal of reference to previous diagrams and formulae ref 21 see 74 There are no built in links so you'll have to navigate manually In my experience this is very tediousTLDR If you're thinking of actually USING this book buy the hard copy NOT the digital one