04355nam 22007575 450 991014434850332120250610110201.03-540-28650-010.1007/b100712(CKB)1000000000212566(DE-He213)978-3-540-28650-9(SSID)ssj0000097729(PQKBManifestationID)11113219(PQKBTitleCode)TC0000097729(PQKBWorkID)10121045(PQKB)10249743(MiAaPQ)EBC3088047(PPN)155207555(MiAaPQ)EBC7252671(EXLCZ)99100000000021256620121227d2004 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierAdvanced Lectures on Machine Learning ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures /edited by Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch1st ed. 2004.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2004.1 online resource (X, 246 p.) Lecture Notes in Artificial Intelligence ;3176Bibliographic Level Mode of Issuance: Monograph3-540-23122-6 Includes bibliographical references and index.An 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.Machine 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.Lecture Notes in Artificial Intelligence ;3176Artificial intelligenceComputer scienceAlgorithmsComputersPattern perceptionArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computer Science, generalhttps://scigraph.springernature.com/ontologies/product-market-codes/I00001Algorithm Analysis and Problem Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/I16021Computation by Abstract Deviceshttps://scigraph.springernature.com/ontologies/product-market-codes/I16013Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XArtificial intelligence.Computer science.Algorithms.Computers.Pattern perception.Artificial Intelligence.Computer Science, general.Algorithm Analysis and Problem Complexity.Computation by Abstract Devices.Pattern Recognition.006.3Bousquet Olivieredthttp://id.loc.gov/vocabulary/relators/edtLuxburg Ulrike vonedthttp://id.loc.gov/vocabulary/relators/edtRätsch Gunnaredthttp://id.loc.gov/vocabulary/relators/edtMachine Learning Summer SchoolBOOK9910144348503321Advanced Lectures on Machine Learning2018032UNINA