Pattern Recognition
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Lecture 1
Introduction to Statistical Pattern Recognition -
Lecture 2
Overview of Pattern Classifiers -
Lecture 3
The Bayes Classifier for Minimising Risk -
Lecture 4
Estimating Bayes Error, Minimax and Neymann-Pearson Classifiers -
Lecture 5
Implementing Bayes Classifier and 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
Nonparametric Estimation, Parzen Windows, Nearest Neighbour Methods -
Lecture 13
Linear Discriminant Functions, Perceptron--Learning Algorithm and Convergence Proof -
Lecture 14
Linear Discriminant Functions; Perceptron -- Learning Algorithm and Convergence Proof -
Lecture 15
AdaLinE and LMS Algorithm; General Nonliner Least-squares Regression
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