EE 602 Course Content
Course Content
1. Structure of statistical reasoning, Introduction to Estimation theory
2. Review of Random variables, vectors, processes, and their statistical description
3. Estimation: Minimum Variance Unbiased Estimator, Cramer Rao Lower Bound (CRLB) for scalar and vector parameters
4. Estimation : Maximum Likeihood Estimation (MLE), Maximum Aposteriori Estimation (MAP), Linear Least Squares (LLSE) with examples of Gaussian mixture modeling (GMM) and Hidden Markov Modeling (HMM)
5. Detection : Introduction, Neyman Pearson theoroem, Binary and Multiple hypothesis testing, Examples
6. Spectrum Estimation : Non Parametric (Periodogram, Welch methods) and Parametric (MVDR method)
7. Any other parts of SSP that are relevant to the above course content
Unless otherwise stated, the content of this page is licensed under Creative Commons Attribution-ShareAlike 3.0 License