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03566aam a22005418i 4500 001 23ABE62CC06F11EC8BCEBD203FECA4DB 003 SILO 005 20220420010009 008 210828s2022 flu b 001 0 eng 010 $a 2021023415 020 $a 1032074019 020 $a 9781032074016 020 $a 0367768585 020 $a 9780367768584 035 $a (OCoLC)1268122074 040 $a LBSOR/DLC $b eng $e rda $c DLC $d OCLCO $d OCLCF $d SILO 042 $a pcc 050 00 $a QC52 $b .R38 2022 082 00 $a 530.0285/53 $2 23 100 1 $a Rauf, Ijaz A., $e author. 245 10 $a Physics of data science and machine learning / $c Ijaz A. Rauf. 250 $a First edition. 263 $a 2109 264 1 $a Boca Raton : $b CRC Press, $c 2022. 300 $a 194 pages : $b illustrations (black and white) ; $c 24 cm 504 $a Includes bibliographical references and index. 505 0 $a An overview of classical mechanics -- An overview of quantum mechanics -- Probabilistic physics -- Design of experiments and analyses -- Basics of machine learning -- Prediction, optimization, and new knowledge development. 520 $a "Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data mining, data science, and machine learning. It also explains how to integrate these techniques into the design of experiments, whilst exploring neural networks and machine learning building on fundamental concepts of statistical and quantum mechanics. This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians, alongside graduate students looking to understand the basic concepts and foundations of data science, machine learning, and artificial intelligence. Although specifically written for physicists, it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid the development of new and innovative machine learning and artificial intelligence tools. Key features: Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt, and adapt. Free from endless derivations, instead equations are presented and explained strategically and explain why"-- $c Provided by publisher. 650 0 $a Physics $x Data processing. 650 0 $a Physics $x Methodology. 650 0 $a Machine learning. 650 0 $a Data mining. 650 0 $a Statistical mechanics. 650 0 $a Quantum statistics. 650 0 $a Probabilities. 650 0 $a Mathematical optimization. 650 7 $a Data mining. $2 fast $0 (OCoLC)fst00887946 650 7 $a Machine learning. $2 fast $0 (OCoLC)fst01004795 650 7 $a Mathematical optimization. $2 fast $0 (OCoLC)fst01012099 650 7 $a Physics $x Data processing. $2 fast $0 (OCoLC)fst01063041 650 7 $a Physics $x Methodology. $2 fast $0 (OCoLC)fst01063075 650 7 $a Probabilities. $2 fast $0 (OCoLC)fst01077737 650 7 $a Quantum statistics. $2 fast $0 (OCoLC)fst01085127 650 7 $a Statistical mechanics. $2 fast $0 (OCoLC)fst01132070 941 $a 1 952 $l UNUX074 $d 20220420010146.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=23ABE62CC06F11EC8BCEBD203FECA4DB 994 $a Z0 $b NIUInitiate Another SILO Locator Search