'Feature Selection and Dimensionality Reduction; Principal Component Analysis' Video Lecture

Feature Selection and Dimensionality Reduction; Principal Component Analysis

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

    • Feature Selection and Dimensionality Reduction; Principal Component Analysis - Click on the Video Link shown below to play the video on Youtube. Browse through Pattern Recognition (Electronics and Communication Engineering) Video Lectures by Prof. P.S. Sastry from IISc Bangalore through NPTEL.

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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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