机器学习与概率图模型中科院自动化所系列报告(王立威)25443.pptx

Machine Learning and Graphical Models王立威北京大学信息科学技术学院(Lecture I)Outline A brief overview of Machine Learning Graphical Models Representation Inference Learning2 Definition of Machine Learning:Learning from experiences.“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P,if its performance at tasks in T,as measured by P,improves with experience E.”-Tom Mitchell3“Classical”Machine Learning Tasks:Classification:spam filter,face recognition,Regression Hooks law,Keplers law,Ranking Search engine Probability(Distribution)Estimation4“Classical”Machine Learning Algorithms Classification SVM Boosting Random Forest Bagging(Deep)Neural Networks Regression Lasso Boosting5Support Vector Machines(SVMs)SVM:the large margin classifier SVM:hinge loss minimization+regularizationBoosting Boosting:(implicit)large margin classifier Boosting:exp loss minimization(+regularization)