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The Bayesian learning scheme is computationally infeasible for most of the unsupervised learning problems. This paper suggests a learning scheme, "learning with a probabilistic teacher," which works with unclassified samples and is computationally feasible for many practical problems. In this scheme a sample is probabilistically assigned with a class with appropriate probabilities computed using all the information available: Then the sample is used in learning the parameter values given this assignment of the class. The convergence of the scheme is established and a comparison with the best linear estimator is presented.