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 - Maximum Likelihood Estimation
Lecture 8 - MLE of Transformed Parameters, Bayesian Estimation
Lecture 9 - Introduction to Detection Theory, Neyman-Pearson Theorem
Lecture 10 - ROC, ML and MAP Detectors, Multiple Hypothesis Testing
Lecture 11 - Detection of Deterministic Signals, Matched Filter
Lecture 12 - Generalized Matched Filter, Multiple Signals, Linear Model
Lecture 13 - Detection of Deterministic Signals under Unknown PDF
Lecture 14 - Wald & Rao Tests, LMP Detector, Energy Detector