Transcription of Introduction to Machine Learning
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Introduction to Machine Learning
Introduction to Machine Learning67577 - Fall, 2008 Amnon ShashuaSchool of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalem, [ ] 23 Apr 2009 Contents1 Bayesian Decision Example: Coin Example: Gaussian Density Bayes Classifier for 2-class Normal Distributions102 Maximum Likelihood/ Maximum Entropy and Empirical Entropy and Duality ML/MaxEnt153EM Algorithm: ML over Mixture of EM Algorithm: with to the Coins Gaussian Mixture and Multinomial Mixture and bag of words Application 274 Support Vector Machines and Kernel Margin Classifier as a Quadratic Linear Programming Support Vector Kernel The Homogeneous Polynomial The non-homogeneous Polynomial The RBF Classifying New Instances39iiiivContents5 Spectral Analysis I: PCA, LDA, : Statistical Maximizing the Variance of Output Decorrelation: Diagonalization of the : Optimal Casen >> s LDA: Basic s LDA: General s LDA: versus Correlation Analysis556 Spectral Analysis II: Algorithm for Matrix Formulation of Clustering: Ratio-Cuts and Normalized-Cuts657 The Formal (PAC) Learning Formal Rectangle Learning of Finite Concept The Realizable The Unrealizable Case778 The VC VC Relation between VC dimension and PAC Learning859 The Double-Sampling Polynomia
Introduction to Machine Learning 67577 - Fall, 2008 Amnon Shashua School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem, Israel
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