The book was delivered quickly and seemed in perfect shape except for one very crucial flaw. I've never before seen such a bizarre flaw in a book in my life. Your recently viewed items and featured recommendations, Select the department you want to search in, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. You can flip the book open to any page, read a sentence or two and be hooked for the next hour or so.” (Peter Rabinovitch, The Mathematical Association of America, May, 2012). Math books, at least data science texts, can usually be divided into those which are easy to read but contain little technical rigor and those which are written with a scientific approach to methodology but are so equation dense that it’s hard to imagine them being read outside an advanced academic setting. Some of the derivations are things you would see on the blackboard of an advanced course in statistics, not machine learning, and take careful notes of. 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'). It is a valuable resource for statisticians and anyone interested in data mining in science or industry. Many examples are given, with a liberal use of color graphics. An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics), Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Pattern Recognition and Machine Learning (Information Science and Statistics), Deep Learning (Adaptive Computation and Machine Learning series), Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series), Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. … this is a welcome update to an already fine book, which will surely reinforce its status as a reference.” (Gilles Blanchard, Mathematical Reviews, Issue 2012 d), “The book would be ideal for statistics graduate students … . This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. Reviewed in the United States on February 4, 2018. Is it a good investment, statistically speaking!" During the past decade there has been an explosion in computation and information technology. 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 factorisation, and spectral clustering. Download The Elements of Statistical Learning: Data Mining, Inference, and Prediction written by Trevor Hastie & Robert Tibshirani and Jerome Friedman is very useful for Mathematics Department students and also who are all having an interest to develop their knowledge in the field of Maths. 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 factorisation, and spectral clustering. 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. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. Please try again. Arrogant but essential; didactic incoherence; an unfriendly book!