Machine perception, pattern recognition systems, design cycle, learning andadaptation, Applications of pattern recognition.
Introduction, probability of events, random variables, Joint distributions anddensities, moments of random variables, estimation of parameters from samples, minimum riskestimators.
Baye’s Theorem, multiple features,conditionally independent features, decision boundaries, unequal costs of error, estimation oferror rates, the leavingone- out technique. Characteristic curves, estimating the composition ofpopulations.
Introduction, histograms, Kernel and windowestimators, nearest neighbor classification techniques, adaptive decision boundaries, adaptivediscriminate Functions, minimum squared error discriminate functions, choosing a decisionmaking technique.
Unsupervised Bayesian learning,data decryption and clustering, criterion functions and clustering, Hierarchical clustering, Onlineclustering, component analysis.
Introduction, nets without hidden layers. nets withhidden layers, the back Propagation algorithms, Hopfield nets, an application.