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Unformatted text preview: 1 CMPSCI 383 Nov 15, 2011 Regression and Classifcation with Linear Models 2 Today ʼ s topics • Learning from Examples: brief review • Univariate Linear Regression • Batch gradient descent • Stochastic gradient descent • Multivariate Linear Regression • Regularization • Linear ClassiFers • Perceptron learning rule • Logistic Regression 3 Learning from Examples (supervised learning) 4 Learning from Examples (supervised learning) 5 Learning from Examples (supervised learning) 6 Learning from Examples (supervised learning) 7 Learning from Examples (supervised learning) 8 Learning from Examples (supervised learning) 9 Important issues • Generalization • Overftting • Crossvalidation • Holdout cross validation • KFold cross validation • Leaveoneout crossvalidation • Model selection 10 Recall Notation ( x 1 , y 1 ), ( x 2 , y 2 ), K ( x N , y N ) training set Where each was generated by an unknown function y j y = f ( x ) Discover a function that best approximates the true function h f hypothesis 11 Loss Functions L ( x , y , ˆ y ) = Utility (result of using y given input x ) − Utility (result of using ˆ y given input x ) Suppose the true prediction for input...
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This note was uploaded on 11/29/2011 for the course COMPSCI 383 taught by Professor Andrewbarto during the Fall '11 term at UMass (Amherst).
 Fall '11
 AndrewBarto
 Artificial Intelligence

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