Study of estimation, detection, and identification methods. Detection: Binary and M-ary hypothesis testing. Neyman-Pearson detection. Invariance. Matched filter, Constant False Alarm Rate (CFAR) matched filter and variants. Bayes detection. Likelihood ratios. Estimation: Maximum-likelihood estimation. Bayes estimation. Sufficiency and invariance. Cramer-Rao bounds. Estimation with the linear statistical model. Minimum mean square error. Recursive estimation. Kalman-Bucy filter. Identification: Maximum-likelihood identification of Auto-Regressive Moving Average (ARMA) models, signal subspaces, parameters in sinusoidal models and Machine learning methods. Topics may vary.