Midterm 10 701 Fall 2006 Outline Statistics for homework 2 Overview of the midterm What you need to know Go over problems from previous midterms General 58 homework submission including 26 late ones last time 64 homeworks Mean 75 3 7 9 15 5 30 6 21 2 Stdev 14 0 Median 76 5 Histogram Overview Open notes open book No electronic devices computer cell phone calculator A lot of short problems varying difficulty but easier than the homeworks Understand materials look for interesting structures in problems What you need to know General Decision trees Probability MLE MAP Linear regression Generative and discriminative classifiers naive Bayes and logistic regression Neural network Model selection Boosting SVM kernel methods PAC learning General Training error Test error Decision boundary Decision trees Concept ID3 Entropy conditional entropy Information gain Probability MLE MAP Axioms of probability Conditional probability Bayes rule Independence conditional independence Likelihood MLE Prior posterior MAP Linear regression Regression vs classification Estimation gradient descent normal equation Probabilistic interpretation Generative and discriminative classifiers Bayes classifier Naive Bayes assumptions Logistic regression assumptions regularization Relationship Generative vs discriminative classifiers Neural networks What is a neural network What is an activation function Hidden layer Backpropagation algorithm Regularization Model selection Purpose Methods cross validation score Bias variance decomposition Feature selection Boosting Ensembles of classifiers AdaBoost SVM Margin Support vectors Kernel PAC learning Size of hypothesis space Epsilon delta VC dimension shattering Questions
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