《工業(yè)互聯(lián)網(wǎng)基礎(chǔ)（雙語(yǔ)）》課件-機(jī)器學(xué)習(xí)

機(jī)器學(xué)習(xí)工業(yè)互聯(lián)網(wǎng)基礎(chǔ)（雙語(yǔ)）Machinelearning機(jī)器學(xué)習(xí)MachineLearningAmulti-disciplinaryfieldthatcombinesmultipledisciplinessuchasprobabilitytheory.Itstudieshowcomputerssimulatehumanlearningbehaviorstoacquirenewknowledge,skills,andimproveperformance多領(lǐng)域交叉學(xué)科，融合概率論等多學(xué)科，研究計(jì)算機(jī)模擬人類學(xué)習(xí)行為，以獲取新知識(shí)、技能并改善性能。監(jiān)督學(xué)習(xí)無(wú)監(jiān)督學(xué)習(xí)強(qiáng)化學(xué)習(xí)supervisedlearningunsupervisedlearningreinforcementlearning概念學(xué)習(xí)conceptlearning監(jiān)督學(xué)習(xí)superviedlearning監(jiān)督學(xué)習(xí)SupervisedLearningLearnapredictivemodelfromlabeledtrainingdata.Thegoalistopredictaccurateoutputsfornewinputdata.Commonalgorithmsincludelinearregression,etc.從有標(biāo)記的訓(xùn)練數(shù)據(jù)中學(xué)習(xí)預(yù)測(cè)模型，目標(biāo)是對(duì)新輸入數(shù)據(jù)預(yù)測(cè)準(zhǔn)確輸出，常見(jiàn)算法有線性回歸等。歸納推理inductivereasoning無(wú)監(jiān)督學(xué)習(xí)unsuperviedlearning無(wú)監(jiān)督學(xué)習(xí)UnsupervisedLearningFindhiddenpatternsorstructuresinunlabeledtrainingdata.Thegoalistodiscovernaturalgroupingsandotherpatternsinthedata.CommonalgorithmsincludeK-Meansclustering,etc.在無(wú)標(biāo)記訓(xùn)練數(shù)據(jù)中找隱藏模式或結(jié)構(gòu)，目標(biāo)是發(fā)現(xiàn)數(shù)據(jù)中的自然分組等規(guī)律，常見(jiàn)算法有K-Means聚類等。強(qiáng)化學(xué)習(xí)reinforcementlearning×√強(qiáng)化學(xué)習(xí)ReinforcementLearningTheagentinteractswiththeenvironmentandadjuststhebehaviorstrategybasedonrewardsandpunishmentstomaximizecumulativerewards.Itemphasizeslearningtheoptimalstrategythroughtrialanderroranddynamicinteraction.智能體與環(huán)境交互，根據(jù)環(huán)境獎(jiǎng)懲調(diào)整行為策略，以最大化累積獎(jiǎng)勵(lì)，強(qiáng)調(diào)試錯(cuò)與動(dòng)態(tài)交互學(xué)習(xí)最優(yōu)策略。自然語(yǔ)言處理NaturalLanguageProcessing圖像和視頻處理ImageandVideoProcessing預(yù)測(cè)分析PredictiveAnalyticsQuestions人類學(xué)習(xí)能夠綜合多種感官和直覺(jué)進(jìn)行判斷，機(jī)器學(xué)習(xí)則依靠精確的數(shù)值計(jì)算，哪種方式更具優(yōu)勢(shì)？Humanlearningiscapableofmakingjudgmentsbyintegratingvarioussensesandintuitions,whilemachine