台大-李宏毅-B站机器学习视频-课件神经网络与深度学习Introduction

Introductionofthiscourse,李宏毅Hung-yiLee,WelcomeourTAs,林資偉,盧柏儒,方為,宋昀蓁,賴顗安,沈家豪,林賢進,敖家維,吳柏瑜,Whatarewegoingtolearn?,WhatisMachineLearning?,“Hi”,“Howareyou”,“Goodbye”,Yousaid“Hello”,Alargeamountofaudiodata,Youwritetheprogramforlearning.,Learning.,“monkey”,“cat”,“dog”,WhatisMachineLearning?,Thisis“cat”,Alargeamountofimages,Youwritetheprogramforlearning.,Learning.,MachineLearningLookingforaFunction,SpeechRecognitionImageRecognitionPlayingGoDialogueSystem,“Cat”,“Howareyou”,“5-5”,“Hello”,“Hi”,(whattheusersaid),(systemresponse),(nextmove),Framework,Asetoffunction,“cat”,“dog”,“money”,“snake”,Model,“cat”,ImageRecognition:,Framework,Asetoffunction,“cat”,ImageRecognition:,Model,TrainingData,Goodnessoffunctionf,Better!,“monkey”,“cat”,“dog”,functioninput:,functionoutput:,Framework,Asetoffunction,“cat”,ImageRecognition:,Model,TrainingData,Goodnessoffunctionf,“monkey”,“cat”,“dog”,Pickthe“Best”Function,Using,“cat”,Training,Testing,Step1,Step2,Step3,LearningMap,SupervisedLearning,Regression,LinearModel,StructuredLearning,Semi-supervisedLearning,TransferLearning,UnsupervisedLearning,ReinforcementLearning,Classification,DeepLearning,SVM,decisiontree,K-NN,Non-linearModel,LearningMap,Regression,Theoutputofthetargetfunctionis“scalar”.,f,今天上午PM2.5,昨天上午PM2.5,.,明天上午PM2.5,HW1,(scalar),9/01上午PM2.5=63,9/02上午PM2.5=65,9/03上午PM2.5=100,TrainingData:,9/12上午PM2.5=30,9/13上午PM2.5=25,9/14上午PM2.5=20,Input:,Input:,Output:,Output:,預測PM2.5,LearningMap,Regression,Classification,Classification,BinaryClassification,Multi-classClassification,Functionf,Functionf,Input,Input,YesorNo,Class1,Class2,ClassN,BinaryClassification,Spamfiltering,(http:/spam-filter-,Function,Yes/No,Yes,No,HW2,TrainingData,Multi-classClassification,http:/top-breaking-,Function,政治,體育,經濟,體育,政治,財經,TrainingData,DocumentClassification,LearningMap,Regression,LinearModel,Classification,DeepLearning,Non-linearModel,Classification-DeepLearning,ImageRecognition,Function,“monkey”,“cat”,“dog”,“monkey”,“cat”,“dog”,TrainingData,Eachpossibleobjectisaclass,HW3,ConvolutionalNeuralNetwork(CNN),Classification-DeepLearning,PlayingGO,Function,(19x19classes),Nextmove,Eachpositionisaclass,一堆棋譜,TrainingData,進藤光v.s.社清春,黑:5之五,白:天元,黑:五之5,Classification-DeepLearning,PlayingGO,Function,(19x19classes),Nextmove,Eachpositionisaclass,一堆棋譜,TrainingData,進藤光v.s.社清春,黑:5之五,白:天元,黑:五之5,Input:黑:5之五,Output:天元,Input:黑:5之五、白:天元,Output:五之5,LearningMap,SupervisedLearning,Regression,LinearModel,Semi-supervisedLearning,Classification,DeepLearning,SVM,decisiontree,K-NN,Non-linearModel,TrainingData:,Input/outputpairoftargetfunction,Functionoutput=label,Semi-supervisedLearning,Labelleddata,Unlabeleddata,cat,dog,(Imagesofcatsanddogs),Forexample,recognizingcatsanddogs,LearningMap,SupervisedLearning,Regression,LinearModel,StructuredLearning,Semi-supervisedLearning,TransferLearning,Classification,DeepLearning,SVM,decisiontree,K-NN,Non-linearModel,TransferLearning,Labelleddata,cat,dog,Datanotrelatedtothetaskconsidered(canbeeitherlabeledorunlabeled),elephant,Haruhi,Forexample,recognizingcatsanddogs,LearningMap,SupervisedLearning,Regression,LinearModel,StructuredLearning,Semi-supervisedLearning,TransferLearning,UnsupervisedLearning,Classification,DeepLearning,SVM,decisiontree,K-NN,Non-linearModel,HW4,http:/top-breaking-,UnsupervisedLearning,MachineReading:Machinelearnsthemeaningofwordsfromreadingalotofdocumentswithoutsupervision,UnsupervisedLearning,Drawsomething!,Ref:,UnsupervisedLearning,Chung&Lee,INTERSPEECH2016,Machinelistenstolotsofaudiobook,HowaboutmachinewatchTV?,LearningMap,SupervisedLearning,Regression,LinearModel,StructuredLearning,Semi-supervisedLearning,TransferLearning,UnsupervisedLearning,Classification,DeepLearning,SVM,decisiontree,K-NN,Non-linearModel,StructuredLearning,BeyondClassification,“機器學習”,“大家好，歡迎大家來修機器學習”,“MachineLearning”,f,SpeechRecognition,f,MachineTranslation,LearningMap,SupervisedLearning,Regression,LinearModel,StructuredLearning,Semi-supervisedLearning,TransferLearning,UnsupervisedLearning,ReinforcementLearning,Classification,DeepLearning,SVM,decisiontree,K-NN,Non-linearModel,ReinforcementLearning,Supervisedv.s.Reinforcement,SupervisedReinforcement,Agent,.,.,Bad,“Hello”,Say“Hi”,“Byebye”,Say“Goodbye”,Learningfromteacher,Learningfromcritics,Supervisedv.s.Reinforcement,Supervised:ReinforcementLearning,Nextmove:“5-5”,Nextmove:“3-3”,Firstmove,manymoves,Win!,AlphaGoissupervisedlearning+reinforcementlearning.,LearningMap,SupervisedLearning,Regression,LinearModel,StructuredLearning,Semi-supervisedLearning,TransferLearning,UnsupervisedLearning,ReinforcementLearning,Classification,DeepLearning,SVM,decisiontree,K-NN,Non-linearModel,scenario,method,task,WhyIneedtolearnMachineLearning?,AI即將取代多數的工作?,NewJobinAIAge,http:/www.express.co.uk/news/science/651202/First-step-towards-The-Terminator-becoming-reality-AI-beats-champ-of-world-s-oldest-game,AI訓練師,機器不是自己會學嗎？為什麼需要AI訓練師,戰鬥是寶可夢在打，為什麼需要寶可夢訓練師？,AI訓練師,寶可夢訓練師,寶可夢訓練師要挑選適合的寶可夢來戰鬥寶可夢有不同的屬性召喚出來的寶可夢不一定聽話E.g.小智的噴火龍需要足夠的經驗,AI訓練師,在step1，AI訓練師要挑選合適的模型不同模型適合處理不同的問題不一定能在step3找出bestfunctionE.g.DeepLearning需要足夠的經驗,AI訓練師,厲害的AI，AI訓練師功不可沒讓我們一起朝AI訓練師之路邁進,.tw/webonly_content_10787.html,Policy,上課教材,以後上課會錄音上課投影片和錄音會放到ceiba和李宏毅的個人網頁上李宏毅的個人網頁：.tw/tlkagk/courses_ML16.html,FB社團,社團:MachineLearning(2016,Fall),評量方式,不點名、不考試作業：沒有分組、每個人都要繳交作