The Elements of Statistical Learning: Data Mining, Inference, and PredictionDuring 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 terminology. 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 should be a 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, support vector machines, classification trees and boostingthe 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, nonnegative 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 rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote apopular book of that title. Hastie codeveloped much of the statistical modeling software and environment in R/SPLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is coauthor of the very successful An Introduction to the Bootstrap. Friedman is the coinventor of many datamining tools including CART, MARS, projection pursuit and gradient boosting. 
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Manual
I own the 2nd edition of this book. The topics are described more from a statistics perspective than the computer science perspective, but as written by statisticians for computer scientists instead of for other statisticians. The examples are interesting and the graphics very nice.
Contents
II  1 
III  9 
IV  11 
V  18 
VI  22 
VII  28 
VIII  32 
IX  33 
LXXII  254 
LXXIII  255 
LXXIV  257 
LXXV  266 
LXXVI  279 
LXXVII  283 
LXXVIII  290 
LXXIX  293 
X  37 
XI  39 
XIII  41 
XIV  42 
XV  50 
XVI  55 
XVII  75 
XX  79 
XXI  81 
XXII  84 
XXIII  95 
XXIV  105 
XXV  111 
XXVII  115 
XXVIII  117 
XXIX  126 
XXX  127 
XXXI  134 
XXXII  137 
XXXIII  138 
XXXIV  144 
XXXV  148 
XXXVI  155 
XXXVIII  160 
XL  163 
XLI  165 
XLII  172 
XLIII  174 
XLIV  175 
XLV  179 
XLVI  182 
XLVII  186 
XLVIII  188 
XLIX  190 
LII  193 
LIII  196 
LIV  200 
LV  203 
LVI  205 
LVII  206 
LVIII  208 
LIX  210 
LX  214 
LXI  217 
LXII  222 
LXIV  225 
LXV  231 
LXVI  235 
LXVII  236 
LXVIII  243 
LXIX  246 
LXX  250 
LXXI  253 
LXXX  295 
LXXXII  296 
LXXXIII  299 
LXXXIV  303 
LXXXV  304 
LXXXVI  305 
LXXXVII  306 
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LXXXIX  312 
XC  314 
XCI  316 
XCII  319 
XCIII  323 
XCIV  324 
XCV  331 
XCVI  335 
XCVII  340 
XCVIII  344 
XCIX  347 
C  350 
CI  353 
CII  355 
CIII  359 
CIV  362 
CV  366 
CVI  367 
CVIII  368 
CIX  371 
CX  377 
CXI  390 
CXII  391 
CXIII  397 
CXIV  399 
CXV  406 
CXVII  411 
CXVIII  415 
CXIX  427 
CXX  432 
CXXI  433 
CXXIII  437 
CXXIV  439 
CXXV  453 
CXXVI  480 
CXXVII  485 
CXXVIII  494 
CXXIX  502 
CXXX  503 
CXXXI  504 
509  
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527  