This course includes modeling uncertainty, Markov decision processes, model-based reinforcement learning, model-free reinforcement learning function approximation, policy gradient, partially observable Markov decision processes.

This course covers discrete and continuous distributions, expectation, sample mean, law of large numbers, variance, sample variances, normal approximation. stochastic dependence and correlation, description of data using statistics and histogramsm, parameter estimation of statistical models, confidence interval, testing of statistical hypotheses