Algorithm算法代写代考

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 28, 2015 Today: • Naïve Bayes • discrete-valued Xi’s • Document classification • Gaussian Naïve Bayes • real-valued Xi’s • Brain image classification Readings: Required: • Mitchell: “Naïve Bayes and Logistic Regression” (available on class website) Optional • Bishop 1.2.4 • Bishop […]

The Boosting Approach to Machine Learning Maria-Florina Balcan 03/16/2015 • • Boosting General method for improving the accuracy of any given learning algorithm. Works by creating a series of challenge datasets s.t. even modest performance on these can be used to produce an overall high-accuracy predictor. • Works amazingly well in practice — Adaboost and

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 28, 2015 Today: • Naïve Bayes • discrete-valued Xi’s • Document classification • Gaussian Naïve Bayes • real-valued Xi’s • Brain image classification Readings: Required: • Mitchell: “Naïve Bayes and Logistic Regression” (available on class website) Optional • Bishop 1.2.4 • Bishop

Semi-Supervised Learning Maria-Florina Balcan 03/30/2015 Readings: • Semi-Supervised Learning. Encyclopedia of Machine Learning. Jerry Zhu, 2010 • Combining Labeled and Unlabeled Data with Co- Training. Avrim Blum, Tom Mitchell. COLT 1998. Learning Algorithm Fully Supervised Learning Data Source Distribution D on X Expert / Oracle Labeled Examples (x1,c*(x1)),…, (xm,c*(xm)) Alg.outputs h:X!Y x1 > 5 c*

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University March 4, 2015 Today: • Graphical models • Bayes Nets: • EM • Mixture of Gaussian clustering • Learning Bayes Net structure (Chow-Liu) Readings: • Bishop chapter 8 • Mitchell chapter 6 Learning of Bayes Nets • Fourcategoriesoflearningproblems – Graph structure may be