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ECE 598: Data Driven Techniques Graduate, Fall semester, Elective, University of Illinois at Urbana-Champaign. Topics: Learning algorithms for equation solving and function optimization, simplified convergence analysis, fair comparison methods. Neural networks, universal approximation, configuration and training. Decision making, Bayesian techniques, data-driven decision making, Bayes-consistent training methods, data-driven decision making for Markov processes. Realization of random variables, classical methods, methods based on transformations, generative networks, adversarial (GANs) 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. Clustering, K-means, Gaussian mixtures, expectation/maximization. Kernels and vector spaces, Mercer kernels, nonlinear function approximation using kernels.

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Past Courses/Lectures Machine Learning: Elective, Department of Electrical and Computer Engineering and Master inter-departmental program: Information Processing Systems and Machine Intelligence, University of Patras, Greece, (undergraduate and graduate course). Digital Signal Processing and Lab: Elective, Department of Electrical and Computer Engineering, University of Patras, Greece, (undergraduate course). Detection and Estimation I and II: Core (I) and Elective (II), Master inter-departmental program: Information Processing Systems and Machine Intelligence, University of Patras, (graduate course). Topics in the Foundations of Computer Science, Foundations of Computer and Data Science: Department of Computer Science, Rutgers University, New Brunswick, NJ, USA, Fall 2018, Spring 2019, Fall 2019, (graduate course). Pattern Recognition, Theory and Applications: Department of Computer Science, Rutgers University, New Brunswick, NJ, USA, Fall 2018, (graduate course). Computer Vision: Department of Computer Science, Rutgers University, New Brunswick, NJ, USA, Fall 2017 and Spring 2018,  2017  (graduate course). Digital Signals and Filters: Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA, Fall 2016,  2016  (graduate course). Optimization modeling: Department of Management Science & Information Systems, Business School, Rutgers University, New Brunswick, Fall 2015, (undergraduate course). Optimal stopping, Application to sequential detection: Coordinated Science Lab, University of Illinois, Urbana-Champaign, Fall 2014,  videos  (series of 11 one-hour lectures). Detection and estimation theory: Department of Electrical Engineering, Columbia University, Fall  2009   and  2012  (graduate course). Sequential detection of changes: Department of Statistics, Columbia University, Summer  2007  (series of lectures). Estimation Theory and Stochastic Control: Department of Electrical and Computer Engineering, University of Patras, Greece, (undergraduate course). Detection & Estimation: Department of Electrical and Computer Engineering, also Department of Computer Engineering and Informatics, University of Patras, Greece, (graduate course). Fuzzy & Neural Control: Department of Electrical and Computer Engineering, University of Patras, Greece, (undergraduate course). Digital Communications: Department of Computer Engineering & Informatics, University of Patras, Greece (undergraduate course). Signals and Systems: Department of Computer Engineering & Informatics, University of Patras, Greece (undergraduate course). Control Theory: Department of Computer Engineering & Informatics, University of Patras, Greece (undergraduate course). Linear Algebra: Department of Computer Engineering & Informatics, University of Patras, Greece (undergraduate course). Circuit Theory: Department of Computer Engineering & Informatics, University of Patras, Greece (undergraduate course). Measure theory and probability: Department of Computer Engineering & Informatics, University of Patras, Greece (series of lectures).

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