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This course covers pattern recognition and machine learning techniques. Topics include Bayesian decision theory, clustering, component analysis, hidden Markov models, linear discriminant functions, maximum-likelihood and Bayesian parameter estimation, neural networks, nonparametric techniques, stochastic methods, and unsupervised learning.
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