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Subject offerings of new and developing areas of knowledge in electrical and computer engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Data Driven Techniques for Decision Making and Estimation Learning algorithms for equation solving and function optimization, simplified convergence analysis, fair comparison methods. Decision making, Bayesian techniques, data-driven decision making, Bayes-consistent training methods, data-driven decision making for Markov processes. Realization of random variables, generative networks, adversarial and non-adversarial design of generative networks, probability density vs generative modeling for random data on manifolds. Parameter estimation, Bayesian and non-Bayesian estimators, data-driven parameter estimation, generative models for robust estimation and efficient solution of high-dimensional inverse problems. Data-driven estimation of conditional expectations, application to stochastic optimization. Prerequisites: Probability theory and elements of random processes