Title:MachineLearning:AnAlgorithmicPerspective,2ndEditionAuthor:StephenMarslandLength:457pagesEdition:2Language:EnglishPublisher:ChapmanandHall/CRCPublicationDate:2014-10-08ISBN-10:1466583282ISBN-13:9781466583283AProven,Hands-OnApproachforStudentswithoutaStrongStatisticalFoundationSincethebest-sellingfirsteditionwaspublished,therehavebeenseveralprominentdevelopmentsinthefieldofmachinelearning,includingtheincreasingworkonthestatisticalinterpretationsofmachinelearningalgorithms.Unfortunately,computersciencestudentswithoutastrongstatisticalbackgroundoftenfindithardtogetstartedinthisarea.Remedyingthisdeficiency,MachineLearning:AnAlgorithmicPerspective,SecondEditionhelpsstudentsunderstandthealgorithmsofmachinelearning.Itputsthemonapathtowardmasteringtherelevantmathematicsandstatisticsaswellasthenecessaryprogrammingandexperimentation.NewtotheSecondEditionTwonewchaptersondeepbeliefnetworksandGaussianprocessesReorganizationofthechapterstomakeamorenaturalflowofcontentRevisionofthesupportvectormachinematerial,includingasimpleimplementationforexperimentsNewmaterialonrandomforests,theperceptronconvergencetheorem,accuracymethods,andconjugategradientoptimizationforthemulti-layerperceptronAdditionaldiscussionsoftheKalmanandparticlefiltersImprovedcode,includingbetteruseofnamingconventionsinPythonSuitableforbothanintroductoryone-semestercourseandmoreadvancedcourses,thetextstronglyencouragesstudentstopracticewiththecode.Eachchapterincludesdetailedexamplesalongwithfurtherreadingandproblems.Allofthecodeusedtocreatetheexamplesisavailableontheauthor’swebsite.TableofContentsChapter1:IntroductionChapter2:PreliminariesChapter3:Neurons,NeuralNetworks,andLinearDiscriminantsChapter4:TheMulti-layerPerceptronChapter5:R
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