Detection, Estimation, and Learning
Detection, estimation, and learning are underlying to many problems in signal processing, communications, and control engineering and foundational to perform data analysis in various disciplines. This course provides an introduction to guiding principles and classical methods of detection and estimation and to inference on data models. The material covers detection theory, parameter and Bayesian estimation, and machine learning applications in classification, regression and unsupervised learning. The target audience are graduate students from all programs with a solid background in probability, linear algebra, calculus and signals and systems (e.g. obtained or refreshed in the “Mathematical Foundations for Electrical and Computer Engineering” course).
Instructor Lutz Lampe