林子雨老师团队2014年暑假拼血奋战70天E不流Machine LearningIntroductionStudy on the CourseraAll Right Reserved : Andrew NgLecturer:MuchDatabase Lab of Xiamen UniversityAug 12,2014
Machine Learning Introduction Study on the Coursera All Right Reserved : Andrew Ng Lecturer:Much Database Lab of Xiamen University Aug 12,2014
MachineLearningGrew out of workin Al(Artificial Intelligence)NewcapabilityforcomputersExamples:Databasemining·Largedatasetsfromgrowthof automation/web· Web click data, medical records, biology, engineering-Applications can't program by hand.· Handwriting recognition, most of Natural LanguageProcessing(NLP),Computer Vision
• Examples: - Database mining • Large datasets from growth of automation/web. • Web click data, medical records, biology, engineering - Applications can’t program by hand. • Handwriting recognition, most of Natural Language Processing (NLP), Computer Vision. • Machine Learning - Grew out of work in AI(Artificial Intelligence) - New capability for computers
Machine Learning Definition. Tom Mitchell (1998) Well-posed LearningProblem:A computer program is said to learn fromexperience E with respect to some task Tand some performance measure P, if itsperformance on T, as measured by P,improves with experience E
Machine Learning Definition • Tom Mitchell (1998) Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E
Suppose your email program watches whichemails you do or do not mark as spam, andbased on that learns how to better filter spamWhat is the task T in this setting?T : Classifying emails as spam or not spamE: Watchingyou label emails as spam or not spamP:Thenumberofemailscorrectlyclassifiedas spam/notspam"Acomputerprogramis saidtolearnfromexperienceEwithrespecttosometaskTandsomeperformancemeasureP,ifitsperformanceonT,asmeasuredbyP,improveswithexperienceE
Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting? T : Classifying emails as spam or not spam E : Watching you label emails as spam or not spam P: The number of emails correctly classified as spam/not spam “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E
Machine Learning Algorithms-Supervised learningUnsupervised learningOthers:Reinforcement learningRecommender systems
Machine Learning Algorithms - Supervised learning - Unsupervised learning - Others: - Reinforcement learning - Recommender systems