人工智能英文術(shù)語詞匯自測題及答案一、單選題（每題2分，共10題）說明：下列每題提供四個選項，請選擇最符合題意的英文術(shù)語。1.WhatisthetermfortheprocessofanAIsystemlearningfromdatawithoutexplicitprogramming?A.SupervisedLearningB.UnsupervisedLearningC.ReinforcementLearningD.DeepLearning2.Whichofthefollowingisthecorrecttermforatypeofneuralnetworkwithmultiplehiddenlayers?A.ConvolutionalNeuralNetwork(CNN)B.RecurrentNeuralNetwork(RNN)C.DeepNeuralNetwork(DNN)D.GenerativeAdversarialNetwork(GAN)3.Whatdoes"FederatedLearning"referto?A.TrainingamodelonasingledatasetB.CollaborativelytrainingmodelsacrossmultipledeviceswithoutsharingrawdataC.Usingpre-trainedmodelsforinferenceD.Ensemblingmultiplemodelsforbetterperformance4.Whichtermdescribesthephenomenonwhereamodelperformswellontrainingdatabutpoorlyonunseendata?A.OverfittingB.UnderfittingC.BiasD.Variance5.Whatisthetermfortheprocessofconvertingnon-structureddataintoastructuredformatforAIanalysis?A.DataPreprocessingB.FeatureEngineeringC.DataNormalizationD.DataAugmentation二、多選題（每題3分，共5題）說明：下列每題提供四個選項，請選擇所有符合題意的英文術(shù)語。6.Whichofthefollowingarecommontechniquesforreducingoverfittinginmachinelearningmodels?A.DropoutB.L1/L2RegularizationC.EarlyStoppingD.BatchNormalization7.WhattermsdescribemethodsforevaluatingtheperformanceofanAImodel?A.AccuracyB.PrecisionC.RecallD.F1-Score8.Whichofthefollowingaretypesofneuralnetworkscommonlyusedincomputervisiontasks?A.ConvolutionalNeuralNetworks(CNNs)B.RecurrentNeuralNetworks(RNNs)C.GenerativeAdversarialNetworks(GANs)D.TransformerNetworks9.WhattermsrelatetotheethicalconsiderationsinAIdevelopment?A.AlgorithmicBiasB.FairnessC.PrivacyD.Transparency10.WhichofthefollowingareexamplesofgenerativemodelsinAI?A.AutoencodersB.VariationalAutoencoders(VAEs)C.GenerativeAdversarialNetworks(GANs)D.DecisionTrees三、填空題（每題4分，共5題）說明：請根據(jù)題意填寫正確的英文術(shù)語。11.Theprocessoftrainingamodelonlabeleddatawherethecorrectoutputisknowniscalled________Learning.12.Atechniqueusedtosplitadatasetintomultiplepartsforcross-validationiscalled________Validation.13.Thetermforamodelthatcanhandlesequentialdataandretainmemoryofpreviousinputsis________.14.Theprocessofadjustingtherangeofdatavaluestoensureuniformityiscalled_________.15.TheethicalprinciplethatAIsystemsshouldbeinterpretableandtheirdecisionsexplainableisknownasthe________Principle.四、簡答題（每題5分，共3題）說明：請簡要解釋下列術(shù)語的含義。16.Explain"TransferLearning."17.Whatis"EnsembleLearning,"andwhyisituseful?18.Describe"DataAugmentation"inthecontextofAI.五、匹配題（每題6分，共2題）說明：將下列術(shù)語與其對應(yīng)的解釋進行匹配。19.-A.Atypeofneuralnetworkthatlearnshierarchicalfeaturesfromdata.-B.Theprocessoffine-tuningapre-trainedmodelforaspecifictask.-C.Amodelthatconsistsofmultiplelayersofneurons.-D.Atechniquewheremodelsaretrainedinparallelandtheirpredictionsarecombined.Matchthefollowingterms:1.ConvolutionalNeuralNetwork(CNN)2.Fine-tuning3.DeepNeuralNetwork(DNN)4.EnsembleLearning20.-A.Amethodtoimprovemodelgeneralizationbygeneratingsyntheticdata.-B.Theprocessofreducingmodelcomplexitytopreventoverfitting.-C.Atechniquewhereamodellearnsfromitsownpredictions.-D.Thepracticeoftrainingamodelondatafrommultiplesources.Matchthefollowingterms:1.DataAugmentation2.Regularization3.Self-supervisedLearning4.Multi-modalLearning答案及解析一、單選題答案1.B.UnsupervisedLearning解析：UnsupervisedLearning（無監(jiān)督學習）是指模型從無標簽數(shù)據(jù)中學習模式，無需人工提供正確答案。2.C.DeepNeuralNetwork(DNN)解析：DNN是指具有多個隱藏層的神經(jīng)網(wǎng)絡(luò)，層數(shù)越多，模型越“深”。3.B.Collaborativelytrainingmodelsacrossmultipledeviceswithoutsharingrawdata解析：FederatedLearning（聯(lián)邦學習）允許在不共享原始數(shù)據(jù)的情況下，通過設(shè)備間的模型協(xié)同訓練實現(xiàn)數(shù)據(jù)隱私保護。4.A.Overfitting解析：Overfitting（過擬合）是指模型對訓練數(shù)據(jù)過度擬合，導致泛化能力下降。5.A.DataPreprocessing解析：DataPreprocessing（數(shù)據(jù)預處理）是將非結(jié)構(gòu)化數(shù)據(jù)轉(zhuǎn)化為可分析的格式的過程。二、多選題答案6.A,B,C解析：Dropout、L1/L2Regularization和EarlyStopping都是減少過擬合的有效方法。7.A,B,C,D解析：Accuracy（準確率）、Precision（精確率）、Recall（召回率）和F1-Score（F1分數(shù)）都是常見的模型評估指標。8.A,C,D解析：CNNs（卷積神經(jīng)網(wǎng)絡(luò)）、GANs（生成對抗網(wǎng)絡(luò)）和TransformerNetworks（Transformer網(wǎng)絡(luò)）在計算機視覺中應(yīng)用廣泛。9.A,B,C,D解析：AlgorithmicBias（算法偏見）、Fairness（公平性）、Privacy（隱私）和Transparency（透明度）都是AI倫理的核心議題。10.A,B,C解析：Autoencoders、VAEs和GANs都屬于生成模型，能夠生成新的數(shù)據(jù)樣本。三、填空題答案11.Supervised解析：SupervisedLearning（監(jiān)督學習）依賴標簽數(shù)據(jù)進行訓練。12.K-Fold解析：K-FoldValidation（K折驗證）是常見的交叉驗證方法。13.RecurrentNeuralNetwork(RNN)解析：RNN（循環(huán)神經(jīng)網(wǎng)絡(luò)）適用于處理序列數(shù)據(jù)。14.Normalization解析：Normalization（歸一化）調(diào)整數(shù)據(jù)范圍以消除量綱影響。15.Explainability解析：ExplainabilityPrinciple（可解釋性原則）強調(diào)AI決策的透明度。四、簡答題答案16.TransferLearning（遷移學習）解析：遷移學習是指將一個模型在某個任務(wù)上學習到的知識應(yīng)用于另一個相關(guān)任