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Electronics & Communication Engineering  Pattern Recognition
   
 
Title: Pattern Recognition
Department: Electronics & Communication Engineering
Author: Prof. P.S. Sastry
University: IISc Bangalore
Type: WebLink
Abstract: Introduction to pattern recognition, introduction to classifier design and supervised learning from data, classification and regression, basics of Bayesian decision theory, Bayes and nearest neighbour classifiers, parametric and non-parametric estimation of density functions, linear discriminant functions, Perceptron, linear least-squares regression, LMS algorithm.
Fisher linear discriminant, introduction to statistical learning theory and empirical risk minimization, non-linear methods for classification and regression, artificial neural networks for pattern classification and regression, multilayer feedforward networks, backpropagation, RBF networks, Optimal separating hyperplanes, Supoort Vector Machines and some variants, Assessing generalization abilities of a classifier, Bias-variance trade-off, crossvalidation, bagging and boosting, AdaBoost algorithm, brief discussion of feature selection and dimensionality reduction methods.
The course is designed for graduate students (i.e. first year ME or research students). The course is intended to give the students a fairly comprehensive view of fundamentals of classification and regression. However, not all topics are covered.
For example, we do not discuss Decision tree classifiers. Also, the course deals with neural networks models only from the point of view of classification and regression. For example, no recurrent neural network models (e.g., Boltzman machine) are included. The main reason for leaving out some topics is to keep the course content suitable for a one semester course.
 
   
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