'Overview of Statistical Learning Theory; Empirical Risk Minimization' Video Lecture

Overview of Statistical Learning Theory; Empirical Risk Minimization

  • Course: Pattern Recognition
  • Discipline: Electronics and Communication Engineering
  • Faculty: Prof. P.S. Sastry
  • Institute: IISc Bangalore

    • NEXT LECTURE >> Consistency of Empirical Risk Minimization
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    Course Video Lectures Introduction to Statistical Pattern Recogniti.. Overview of Pattern Classifiers The Bayes Classifier for minimizing Risk Estimating Bayes Error; Minimax and Neymann-.. Implementing Bayes Classifier; Estimation of.. Maximum Likelihood estimation of different de.. Bayesian estimation of parameters of density .. Bayesian Estimation examples; the exponential.. Sufficient Statistics; Recursive formulation .. Mixture Densities, ML estimation and EM algor.. Convergence of EM algorithm; overview of Nonp.. Convergence of EM algorithm, Overview of Nonp.. Nonparametric estimation, Parzen Windows, ne.. Linear Discriminant Functions; Perceptron --.. Linear Least Squares Regression; LMS algorith.. AdaLinE and LMS algorithm; General nonliner .. Logistic Regression; Statistics of least squa.. Fisher Linear Discriminant Linear Discriminant functions for multi-class.. Learning and Generalization; PAC learning fra.. Overview of Statistical Learning Theory; Empi.. Consistency of Empirical Risk Minimization Consistency of Empirical Risk Minimization; V.. Complexity of Learning problems and VC-Dimens.. VC-Dimension Examples; VC-Dimension of hyperp.. Overview of Artificial Neural Networks Multilayer Feedforward Neural networks with S.. Backpropagation Algorithm; Representational a.. Feedforward networks for Classification and R.. Radial Basis Function Networks; Gaussian RBF .. Learning Weights in RBF networks; K-means clu.. Support Vector Machines -- Introduction, obt.. SVM formulation with slack variables; nonlin.. Kernel Functions for nonlinear SVMs; Mercer .. Support Vector Regression and ?-insensitive.. Overview of SMO and other algorithms for SVM;.. Positive Definite Kernels; RKHS; Representer.. Feature Selection and Dimensionality Reductio.. No Free Lunch Theorem; Model selection and m.. Assessing Learnt classifiers; Cross Validatio.. Bootstrap, Bagging and Boosting; Classifier E.. Risk minimization view of AdaBoost
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