Lecture 1 - Background and Introduction
Lecture 2 - MVU Estimators & CRLB
Lecture 3 - CRLB Vector Case, General Gaussian Case, Parameter Transformation
Lecture 4 - General CRLB Case, Linear Models
Lecture 5 - General MVU Estimation, RBLS Theorem
Lecture 6 - General Vector MVU Estimation, BLUE
Lecture 7 - Introduction to Maximum Likelihood Estimation
Lecture 8 - MLE of Transformed Parameters, Introduction to Detection Theory
Lecture 9 - Neyman-Pearson Theorem, Receiver Operating Characteristics (ROC)
Lecture 10 - ML and MAP Detectors, Matched Filter
Lecture 11 - Performance of Matched Filter, Generalized Matched Filter