LEADER 04104nam 2200613 450 001 9910260600303321 005 20200520144314.0 010 $a0-262-32575-6 010 $a0-262-32574-8 035 $a(CKB)3710000000226686 035 $a(OCoLC)889884227 035 $a(CaPaEBR)ebrary10919034 035 $a(SSID)ssj0001334629 035 $a(PQKBManifestationID)12584713 035 $a(PQKBTitleCode)TC0001334629 035 $a(PQKBWorkID)11271417 035 $a(PQKB)11300783 035 $a(MiAaPQ)EBC3339851 035 $a(CaBNVSL)mat06895440 035 $a(IDAMS)0b00006482734967 035 $a(IEEE)6895440 035 $a(Au-PeEL)EBL3339851 035 $a(CaPaEBR)ebr10919034 035 $a(EXLCZ)993710000000226686 100 $a20151223d2014 uy 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntroduction to machine learning /$fEthem Alpaydin 205 $aThird edition. 210 1$aCambridge, Massachusetts :$cMIT Press,$d[2014] 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2014] 215 $a1 online resource (xxii, 616 pages) $cillustrations 225 1 $aAdaptive computation and machine learning series 300 $aIncludes index. 311 $a0-262-02818-2 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments. 330 $aThe goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. 410 0$aAdaptive computation and machine learning. 606 $aMachine learning 608 $aElectronic books. 615 0$aMachine learning. 676 $a006.3/1 700 $aAlpaydin$b Ethem$0754614 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910260600303321 996 $aIntroduction to machine learning$91518558 997 $aUNINA