分为作业和答案两部分，分开的资源，不用因为有答案影响自己的思路

首先，来看看CSDN的知识图谱http://lib.csdn.net/base/machinelearning/structure万般喜欢这个插件，于是就翻看源代码，抄了下来

UCBMichaelJordan写的introductiontographicalmodel,入门级介绍。
做machinelearning的同学可以看看。
一共12章，未出版手稿

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

NeuralNetworksandLearningMachines(3rdEdition).pdf这本是全英文的文字版资源。
大家如果学习machinelearning的话，建议自己看英文的，毕竟这东西国外比国内要先进得多，不能让英语成为障碍。
而且，原版的东西绝对比翻译的要准确些，无论翻译的水平有多高。

包包含李宏毅老师的机器学习所有课件，都是在李宏毅老师个人主页下载整理的，建议配合B站李宏毅老师的MachineLearning视频一块使用。

PRML究竟有何过人之处，不能光看它本身的内容，最重要的是看其在MachineLearning,DataMining这一类书籍中的地位。
通常从一个CS小白开始学MachineLearning或者DataMining，到成长为一个独当一面的大牛，都会看几种类型的书

机器学习시작!!!机器学习1일차이이다!!에에문룰들룰들룰들다다다다다다다다다다다다다다다다다다다监督学习＞＞标签주기적으로적으（训练数据集）ex）고양이사고양이label을구별한다例）regression로투자프르그램프르그램，-휴대폰배터리측정-回归（회귀）回归（회귀）란이란：변수변수해해측측해터나터나터나터나영향영향영향영향영향영향영향영향용이용无监督学习＞＞Super이터를보고tensorflow기초tensorflow会话（Session）会话（Session.run）tensorflow发行人Tensor（）发行人!!（Session을!!같다같같같같같같）））））시점에서는v2이고Session에서만v1용사때문에기때문에importtensorflow.compat.v1astf\ntf.disable_v2_behavi

PrefaceIwrotethisbooktohelpmachinelearningpractitioners,likeyou,getontopoflinearalgebra,fast.LinearAlgebraIsImportantinMachineLearningThereisnodoubtthatlinearalgebraisimportantinmachinelearning.Linearalgebraisthemathematicsofdata.It’sallvectorsandmatricesofnumbers.Modernstatisticsisdescribedusingthenotationoflinearalgebraandmodernstatisticalmethodsharnessthetoolsoflinearalgebra.Modernmachinelearningmethodsaredescribedthesameway,usingthenotationsandtoolsdrawndirectlyfromlinearalgebra.Evensomeclassicalmethodsusedinthefield,suchaslinearregressionvialinearleastsquaresandsingular-valuedecomposition,arelinearalgebramethods,andothermethods,suchasprincipalcomponentanalysis,werebornfromthemarriageoflinearalgebraandstatistics.Toreadandunderstandmachinelearning,youmustbeabletoreadandunderstandlinearalgebra.PractitionersStudyLinearAlgebraTooEarlyIfyouaskhowtogetstartedinmachinelearning,youwillverylikelybetoldtostartwithlinearalgebra.Weknowthatknowledgeoflinearalgebraiscriticallyimportant,butitdoesnothavetobetheplacetostart.Learninglinearalgebrafirst,thencalculus,probability,statistics,andeventuallymachinelearningtheoryisalongandslowbottom-uppath.Abetterfitfordevelopersistostartwithsystematicproceduresthatgetresults,andworkbacktothedeeperunderstandingoftheory,usingworkingresultsasacontext.Icallthisthetop-downorresults-firstapproachtomachinelearning,andlinearalgebraisnotthefirststep,butperhapsthesecondorthird.PractitionersStudyTooMuchLinearAlgebraWhenpractitionersdocirclebacktostudylinearalgebra,theylearnfarmoreofthefieldthanisrequiredfororrelevanttomachinelearning.Linearalgebraisalargefieldofstudythathastendrilsintoengineering,physicsandquantumphysics.Therearealso

个人机器学习(MachineLearning,ML)笔记

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。