Probabilistic Systems and Signal Processing

Catalog description

Many real-world phenomena and systems are probabilistic in nature, where the outcome of an experiment has uncertainty. Examples can be found everywhere including medical diagnosis and spread of disease, electronic devices, communication and information systems, internet traffic and social networks, gambling, financial markets, polling and elections, renewable energy, sports, etc. The modeling and analysis of probabilistic systems involve the fields of probability theory, statistics, machine learning, and statistical signal processing. This course covers the basic concepts and techniques of probability theory with application to statistics, machine learning, and statistical signal processing. Topics include probability and counting, conditional probability, Bayes rule, independence, random variables and processes, expectation and correlation, variance and covariance, conditional expectation, signal estimation, limit theorems, minimum mean square error estimation, linear estimation, and confidence intervals. Coursework includes computational problems in MATLAB with automatically generated and real data.