ECE 498 MR

ECE 498 MR - Intro to Stochastic Systems

Spring 2016

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Intro to Stochastic SystemsECE498MR63788LEC31100 - 1220 M W  4070 Electrical & Computer Eng Bldg Maxim Raginsky
Intro to Stochastic SystemsECE498MR263789LEC41100 - 1220 M W  4070 Electrical & Computer Eng Bldg 

Official Description

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.

Section Description

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.

Course Director

Description

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.

Last updated

10/14/2015