Pattern Recognition (USB)

In stock

Media Storage Type : 32 GB USB Stick

NPTEL Subject Matter Expert : Prof. P.S. Sastry

NPTEL Co-ordinating Institute : IISc Bangalore

NPTEL Lecture Count : 42

NPTEL Course Size : 12 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)

Lecture Titles:

Lecture 1 - Introduction to Statistical Pattern Recognition
Lecture 2 - Overview of Pattern Classifiers
Lecture 3 - The Bayes Classifier for minimizing Risk
Lecture 4 - Estimating Bayes Error; Minimax and Neymann-Pearson classifiers
Lecture 5 - Implementing Bayes Classifier; Estimation of Class Conditional Densities
Lecture 6 - Maximum Likelihood estimation of different densities
Lecture 7 - Bayesian estimation of parameters of density functions, MAP estimates
Lecture 8 - Bayesian Estimation examples; the exponential family of densities and ML estimates
Lecture 9 - Sufficient Statistics; Recursive formulation of ML and Bayesian estimates
Lecture 10 - Mixture Densities, ML estimation and EM algorithm
Lecture 11 - Convergence of EM algorithm; overview of Nonparametric density estimation
Lecture 12 - Convergence of EM algorithm, Overview of Nonparametric density estimation
Lecture 13 - Nonparametric estimation, Parzen Windows, nearest neighbour methods
Lecture 14 - Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof
Lecture 15 - Linear Least Squares Regression; LMS algorithm
Lecture 16 - AdaLinE and LMS algorithm; General nonliner least-squares regression
Lecture 17 - Logistic Regression; Statistics of least squares method; Regularized Least Squares
Lecture 18 - Fisher Linear Discriminant
Lecture 19 - Linear Discriminant functions for multi-class case; multi-class logistic regression
Lecture 20 - Learning and Generalization; PAC learning framework
Lecture 21 - Overview of Statistical Learning Theory; Empirical Risk Minimization
Lecture 22 - Consistency of Empirical Risk Minimization
Lecture 23 - Consistency of Empirical Risk Minimization; VC-Dimension
Lecture 24 - Complexity of Learning problems and VC-Dimension
Lecture 25 - VC-Dimension Examples; VC-Dimension of hyperplanes
Lecture 26 - Overview of Artificial Neural Networks
Lecture 27 - Multilayer Feedforward Neural networks with Sigmoidal activation functions;
Lecture 28 - Backpropagation Algorithm; Representational abilities of feedforward networks
Lecture 29 - Feedforward networks for Classification and Regression; Backpropagation in Practice
Lecture 30 - Radial Basis Function Networks; Gaussian RBF networks
Lecture 31 - Learning Weights in RBF networks; K-means clustering algorithm
Lecture 32 - Support Vector Machines -- Introduction, obtaining the optimal hyperplane
Lecture 33 - SVM formulation with slack variables; nonlinear SVM classifiers
Lecture 34 - Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels
Lecture 35 - Support Vector Regression and ?-insensitive Loss function, examples of SVM learning
Lecture 36 - Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizer
Lecture 37 - Positive Definite Kernels; RKHS; Representer Theorem
Lecture 38 - Feature Selection and Dimensionality Reduction; Principal Component Analysis
Lecture 39 - No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off
Lecture 40 - Assessing Learnt classifiers; Cross Validation;
Lecture 41 - Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost
Lecture 42 - Risk minimization view of AdaBoost

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