Pattern Recognition

Lecture Details Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IISc Bangalore. For more details on NPTEL visit httpnptel.ac.in

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Last updated Wed, 08-Jun-2022
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Course overview
Overview

Contents:
Overview of Pattern classification and regression : Introduction to Statistical Pattern Recognition - Overview of Pattern Classifiers
Bayesian decision making and Bayes Classifier : The Bayes Classifier for minimizing Risk - Estimating Bayes Error; Minimax and Neymann-Pearson classifiers
Parametric Estimation of Densities : Implementing Bayes Classifier; Estimation of Class Conditional Densities - Maximum Likelihood estimation of different densities - Bayesian estimation of parameters of density functions, MAP estimates - Bayesian Estimation examples; the exponential family of densities and ML estimates - Sufficient Statistics; Recursive formulation of ML and Bayesian estimates
Mixture Densities and EM Algorithm : Mixture Densities, ML estimation and EM algorithm - Convergence of EM algorithm; overview of Nonparametric density estimation

Nonparametric density estimation : Convergence of EM algorithm; overview of Nonparametric density estimation - Nonparametric estimation, Parzen Windows, nearest neighbour methods
Linear models for classification and regression : Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof - Linear Least Squares Regression; LMS algorithm - AdaLinE and LMS algorithm; General nonliner least-squares regression - Logistic Regression; Statistics of least squares method; Regularized Least Squares - Fisher Linear Discriminant - Linear Discriminant functions for multi-class case; multi-class logistic regression
Overview of statistical learning theory, Empirical Risk Minimization and VC-Dimension : Learning and Generalization; PAC learning framework - Overview of Statistical Learning Theory; Empirical Risk Minimization - Consistency of Empirical Risk Minimization - Consistency of Empirical Risk Minimization; VC-Dimension - Complexity of Learning problems and VC-Dimension - VC-Dimension Examples; VC-Dimension of hyperplanes

Artificial Neural Networks for Classification and regression : Overview of Artificial Neural Networks - Multilayer Feedforward Neural networks with Sigmoidal activation functions; - Backpropagation Algorithm; Representational abilities of feedforward networks - Feedforward networks for Classification and Regression; Backpropagation in Practice - Radial Basis Function Networks; Gaussian RBF networks - Learning Weights in RBF networks; K-means clustering algorithm
Support Vector Machines and Kernel based methods : Support Vector Machines -- Introduction, obtaining the optimal hyperplane - SVM formulation with slack variables; nonlinear SVM classifiers - Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels - Support Vector Regression and -insensitive Loss function, examples of SVM learning - Overview of SMO and other algorithms for SVM; -SVM and -SVR; SVM as a risk minimizer - Positive Definite Kernels; RKHS; Representer Theorem

Feature Selection, Model assessment and cross-validation : Feature Selection and Dimensionality Reduction; Principal Component Analysis - No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off - Assessing Learnt classifiers; Cross Validation;
Boosting and Classifier ensembles : Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost - Risk minimization view of AdaBoost

Curriculum for this course
41 Lessons 39:50:28 Hours
Pattern Recognition
41 Lessons 39:50:28 Hours
  • Introduction to Statistical Pattern Recognition
    Preview 00:54:59
  • Overview of Pattern Classifiers
    00:55:38
  • The Bayes Classifier for minimizing Risk
    00:56:41
  • Estimating Bayes Error; Minimax and Neymann-Pearson classifiers
    00:57:15
  • Implementing Bayes Classifier; Estimation of Class Conditional Densities
    00:58:07
  • Maximum Likelihood estimation of different densities
    00:58:15
  • Bayesian estimation of parameters of density functions, MAP estimates
    00:57:05
  • Sufficient Statistics; Recursive formulation of ML and Bayesian estimates
    00:58:06
  • Mixture Densities, ML estimation and EM algorithm
    00:57:27
  • Convergence of EM algorithm; overview of Nonparametric density estimation
    00:58:17
  • Nonparametric estimation, Parzen Windows, nearest neighbour methods
    00:57:29
  • Linear Discriminant Functions; Perceptron.Learning Algorithm and convergence proof
    00:58:22
  • Linear Least Squares Regression; LMS algorithm
    00:58:15
  • AdaLinE and LMS algorithm; General nonliner least-squares regression
    00:58:17
  • Logistic Regression; Statistics of least squares method; Regularized Least Squares
    00:58:22
  • Fisher Linear Discriminant
    00:58:11
  • Linear Discriminant functions for multi-class case; multi-class logistic regression
    00:57:24
  • Learning and Generalization; PAC learning framework
    00:59:01
  • Overview of Statistical Learning Theory; Empirical Risk Minimization
    00:58:52
  • Consistency of Empirical Risk Minimization
    00:58:34
  • Consistency of Empirical Risk Minimization; VC-Dimension
    00:58:13
  • Complexity of Learning problems and VC-Dimension
    00:58:37
  • VC-Dimension Examples; VC-Dimension of hyperplanes
    00:58:59
  • Overview of Artificial Neural Networks
    00:59:10
  • Multilayer Feedforward Neural networks with Sigmoidal activation functions;
    00:58:56
  • Backpropagation Algorithm; Representational abilities of feedforward networks
    00:59:00
  • Feedforward networks for Classification and Regression; Backpropagation in Practice
    00:58:39
  • Radial Basis Function Networks; Gaussian RBF networks
    00:58:03
  • Learning Weights in RBF networks; K-means clustering algorithm
    00:59:01
  • Support Vector Machines -- Introduction, obtaining the optimal hyperplane
    00:58:54
  • SVM formulation with slack variables; nonlinear SVM classifiers
    00:59:00
  • Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels
    00:58:45
  • Support Vector Regression and ?-insensitive Loss function, examples of SVM learning
    00:58:38
  • Overview of SMO and other algorithms:VM and SVR; SVM as a risk minimizer
    00:58:28
  • Positive Definite Kernels; RKHS; Representer Theorem
    00:58:45
  • Feature Selection and Dimensionality Reduction; Principal Component Analysis
    00:59:13
  • No Free Lunch Theorem; Model selection and model estimation
    00:59:52
  • Assessing Learnt classifiers; Cross Validation;
    00:59:49
  • Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost
    00:59:30
  • Radial Basis Function Networks; Gaussian RBF networks
    00:58:04
  • Linear Least Squares Regression; LMS algorithm
    00:58:15
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