LEADER 04347nam 22007455 450 001 996465660903316 005 20200630091550.0 010 $a3-540-28650-0 024 7 $a10.1007/b100712 035 $a(CKB)1000000000212566 035 $a(DE-He213)978-3-540-28650-9 035 $a(SSID)ssj0000097729 035 $a(PQKBManifestationID)11113219 035 $a(PQKBTitleCode)TC0000097729 035 $a(PQKBWorkID)10121045 035 $a(PQKB)10249743 035 $a(MiAaPQ)EBC3088047 035 $a(PPN)155207555 035 $a(EXLCZ)991000000000212566 100 $a20121227d2004 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Lectures on Machine Learning$b[electronic resource] $eML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures /$fedited by Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch 205 $a1st ed. 2004. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2004. 215 $a1 online resource (X, 246 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v3176 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-23122-6 320 $aIncludes bibliographical references and index. 327 $aAn Introduction to Pattern Classification -- Some Notes on Applied Mathematics for Machine Learning -- Bayesian Inference: An Introduction to Principles and Practice in Machine Learning -- Gaussian Processes in Machine Learning -- Unsupervised Learning -- Monte Carlo Methods for Absolute Beginners -- Stochastic Learning -- to Statistical Learning Theory -- Concentration Inequalities. 330 $aMachine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning. 410 0$aLecture Notes in Artificial Intelligence ;$v3176 606 $aArtificial intelligence 606 $aComputer science 606 $aAlgorithms 606 $aComputers 606 $aPattern recognition 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Science, general$3https://scigraph.springernature.com/ontologies/product-market-codes/I00001 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aArtificial intelligence. 615 0$aComputer science. 615 0$aAlgorithms. 615 0$aComputers. 615 0$aPattern recognition. 615 14$aArtificial Intelligence. 615 24$aComputer Science, general. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aComputation by Abstract Devices. 615 24$aPattern Recognition. 676 $a006.3 702 $aBousquet$b Olivier$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLuxburg$b Ulrike von$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRätsch$b Gunnar$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aMachine Learning Summer School 906 $aBOOK 912 $a996465660903316 996 $aAdvanced Lectures on Machine Learning$92018032 997 $aUNISA