Stat841f09 - Wiki Course Notes

# Stat841f09 - Wiki Course Notes - Stat841 Wiki Cour se Notes...

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10/09/2013 Stat841 - Wiki Course Notes wikicoursenote.com/w/index.php?title=Stat841&printable=yes 1/74 Stat841 From Wiki Course Notes Contents 1 Proposal 2 Mark your contribution here 3 Scribe sign up 4 Classfication-2009.9.30 4.1 Classification 4.2 Error rate 4.3 Bayes Classifier 4.4 Bayesian vs. Frequentist 5 Linear and Quadratic Discriminant Analysis - October 2,2009 5.1 Introduction 5.1.1 Notation 5.1.2 Approaches 5.2 LDA 5.2.1 Motivation 5.2.2 History 5.2.3 Definition 5.2.4 Limitation 5.3 QDA 6 Linear and Quadratic Discriminant Analysis cont'd - October 5, 2009 6.1 Summarizing LDA and QDA 6.2 In practice 6.3 Computation 6.4 The Number of Parameters in LDA and QDA 7 LDA and QDA in Matlab - October 7, 2009 8 Trick: Using LDA to do QDA - October 7, 2009 8.1 Motivation 8.2 Theoretically 8.3 By Example 9 Introduction to Fisher's Discriminant Analysis - October 7, 2009 9.1 Contrasting FDA with PCA 9.2 Intuitive Description of FDA 9.3 Example in R 9.4 Distance Metric Learning VS FDA 10 Fisher's Discriminant Analysis (FDA) - October 9, 2009 10.1 Two-class problem 10.1.1 Between class covariance 10.1.2 Within class covariance 10.1.3 Objective Function 10.1.4 FDA vs. PCA Example in Matlab 10.1.5 Practical example of 2_3 10.1.6 An extension of Fisher's discriminant analysis for stochastic processes 11 FDA for Multi-class Problems - October 14, 2009 11.1 FDA method for Multi-class Problems 11.2 Generalization of Fisher's Linear Discriminant 12 Linear Regression Models - October 14, 2009 12.1 A linear regression example in Matlab 12.2 Comments about Linear regression model 13 Logistic Regression- October 16, 2009 13.1 logistic function 13.2 Intuition behind Logistic Regression 13.3 The Logistic Regression Model 13.4 Fitting a Logistic Regression 13.4.1 Advantages and Disadvantages 13.4.2 Extension

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10/09/2013 Stat841 - Wiki Course Notes wikicoursenote.com/w/index.php?title=Stat841&printable=yes 2/74 14 Logistic Regression(2) - October 19, 2009 14.1 Logistic Regression Model 14.2 Find 14.2.1 WLS 14.2.2 Pseudo Code 14.3 Comparison with Linear Regression 14.4 Comparison with LDA 14.4.1 By example 15 2009.10.21 15.1 Multi-Class Logistic Regression 15.2 Multi-class kernel logistic regression 15.3 Perceptron (Foundation of Neural Network) 15.3.1 Separating Hyperplane Classifiers 15.3.2 Perceptron 15.3.3 A Perceptron Example 16 The Perceptron (Lecture October 23, 2009) 16.1 Problems with the Algorithm and Issues Affecting Convergence 16.2 Comment on gradient descent algorithm 17 Neural Networks (NN) - October 28, 2009 17.1 Introduction 17.2 Activation Function 17.3 Back-propagation 18 Neural Networks (NN) - October 30, 2009 18.1 Back-propagation 18.1.1 How to initialize the weights 18.1.2 How to set learning rates 18.1.3 How to determine the number of hidden units 18.2 Dimensionality reduction application 18.3 Deep Neural Network 18.3.1 Difficulties of training deep architecture [12] 18.4 Neural Networks in Practice 18.5 Issues with Neural Network 18.6 BUSINESS APPLICATIONS OF NEURAL NETWORKS 19 Complexity Control October 30, 2009 20 Complexity Control - Nov 2, 2009 20.1 How do we choose a good classifier?
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