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Pattern Recognition

3 Credit Hour Course
Intended For Level 4 Term 2 Students

Prerequisite: None

Pattern Recognition: introduction, importance; Statistical and Neural Pattern Recognition: Bayesian classifier, Bayes decision theory, discriminant functions and decision surfaces; Bayesian classifier for normal distributions; Linear classifiers: discriminant functions and decision hyperplanes, Perceptron algorithm and its variants, Kessler’s construction; Nonlinear classifiers: two and three layer perceptrons, backpropagation algorithm and its variants; Template matching: optimal path searching techniques, dynamic programming methods, correlation based matching and 2D log search algorithm for image matching; Context dependent classification: Viterbi algorithm, channel equalization, observable and hidden Markov models, three problems of HMM and their application in speech recognition; Syntactic Pattern Recognition: introduction to Syntactic Pattern Recognition, grammar-based approach, parsing, graph-based approach; Unsupervised classification: basic concepts of clustering, proximity measures, categories of clustering algorithms, sequential clustering algorithms.

Department of Computer Science and Engineering, ECE building, Palashi, Dhaka, Bangladesh. The Department is part of the Faculty of Electrical and Electronic Engineering at the Bangladesh University of Engineering & Technology. No part or content of this website may be copied or reproduced without permission of the department authority. Contact info@cse.buet.ac.bd with questions or comments on this page.  [Development Credits]