02559 a2200229 4500020001800000040001500018041000800033082001700041100002100058245006100079260003300140300001300173440004700186490003400233520180900267650002102076650002502097650002402122942000702146999001902153952015702172 a9781119439196 cIIT Kanpur aeng a006.31bK77m aKnox, Steven W. aMachine learningba concise introductioncSteven W. Knox bJohn Wileyc2018aNew Jersey axv, 320p aWiley series in probability and statistics a / edited by David J. Balding aMachine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection— essential elements of most applied projects. This important resource:
Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods
Presents R source code which shows how to apply and interpret many of the techniques covered
Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions
Contains useful information for effectively communicating with clients
A volume in the popular Wiley Series in Probability and Statistics, Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning.
STEVEN W. KNOX holds a Ph.D. in Mathematics from the University of Illinois and an M.S. in Statistics from Carnegie Mellon University. He has over twenty years’ experience in using Machine Learning, Statistics, and Mathematics to solve real-world problems. He currently serves as Technical Director of Mathematics Research and Senior Advocate for Data Science at the National Security Agency. aMachine learning aStatistical learning aPredictive modeling cBK c560880d560880 0010406006_310000000000000_K77M708GEN9900218aIITKbIITKd2019-11-04e2g5717.14l12m52o006.31 K77mpA184885r2024-02-26s2024-01-23v7146.43yBK