Special Topics in ME: Research Topics in Machine Learning and System Identification

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Introduces students to research topics at the interface of system identification, data science, and machine learning. Reviews linear algebra, probability and information theory, and general concepts from learning algorithms. Introduction to the notion of estimators, maximum-likelihood estimation, stochastic gradient descent, and algorithm design. Provides an overview of deep feedforward networks, regularization strategies for deep learning, and optimization strategies for learning architectures. Introduces topics in system identification and reviews past and present approaches for using neural networks to perform system identification.