ECE 498 MR

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: 0 to 4 undergraduate hours. 0 to 4 graduate hours. May be repeated in the same or separate terms if topics vary.

Exploration of noise, uncertainty, and randomness in the context of signals and systems. The course will introduce discrete- and continuous-time random processes as input and/or output signals of various types of systems, with and without memory or feedback. Probabilistic notions will be integrated with techniques from signals and systems, such as linearity, time-invariance, causality, transform methods, and stability. Basic concepts will be illustrated via numerous examples, such as noise in linear and nonlinear circuits, average consensus and PageRank, queuing systems, noise in remote sensing applications, Bayesian filtering, Monte Carlo simulation, risk allocation in financial portfolios, stochastic gradient descent in machine learning. Prerequisites: ECE 310 and ECE 313.

Exploration of noise, uncertainty, and randomness in the context of signals and systems. The course will introduce discrete- and continuous-time random processes as input and/or output signals of various types of systems, with and without memory or feedback. Probabilistic notions will be integrated with techniques from signals and systems, such as linearity, time-invariance, causality, transform methods, and stability. Basic concepts will be illustrated via numerous examples, such as noise in linear and nonlinear circuits, average consensus and PageRank, queuing systems, noise in remote sensing applications, Bayesian filtering, Monte Carlo simulation, risk allocation in financial portfolios, stochastic gradient descent in machine learning.

10/14/2015