000 | 02398 a2200217 4500 | ||
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020 | _a9781119439196 | ||
040 | _cIIT Kanpur | ||
041 | _aeng | ||
082 |
_a006.31 _bK77m |
||
100 | _aKnox, Steven W. | ||
245 |
_aMachine learning _ba concise introduction _cSteven W. Knox |
||
260 |
_bJohn Wiley _c2018 _aNew Jersey |
||
300 | _axv, 320p | ||
440 | _aWiley series in probability and statistics | ||
490 | _a / edited by David J. Balding | ||
520 | _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. | ||
650 | _aMachine learning | ||
650 | _aStatistical learning | ||
650 | _aPredictive modeling | ||
942 | _cBK | ||
999 |
_c560880 _d560880 |