Lectures (given on campus, but can be followed on Zoom by connecting to Platon. Meeting Id: 69257914537)

Feb. 18, 10.15 - 12.00, J3506 Platon. Lecture 0 (optional): Brief review of probability and mathematical statistics. Some linear algebra basics. Handout material: ProbabilityBasicsLecture.pdf (pages 1-23), Zoom Notes for Linear Algebra.pdf (Sections 3.5, 3.7, 4.1, 6.3) and Primer on Random Processes.pdf. See also Appendix A.1 in Kay's book.
Feb. 25, 10.15 - 12.00, J3506 Platon. Lecture 1: Planning, Introduction. Chapter 1. Minimum variance unbiased estimation (MVUE). The Cramér-Rao Lower Bound (CRLB). Chapters 2-3. Lecture notes: Lecture 1.pdf
Mar. 4, 10.15 - 12.00, J3506 Platon. Lecture 2: Linear models. Best linear unbiased estimation (BLUE). Chapters 4 and 6. Lecture notes: Lecture 2.pdf Notes from the Smart Board during Lecture 2; proof of BLUE, the general case. It remains to prove that the solution is identical to the one given in the book and in the lectures. It is enough to do for C=I (white noise): Proof_BLUE_general.pdf
Mar. 11, 10.15 - 12.00, J3506 Platon. Lecture 3: Sufficient statistics. Maximum Likelihood Estimation (MLE). Chapters 5 and 7. Lecture 3.pdf Notes from the Smart Board: "proof" of asymptotic properties of MLE: Proof_MLE_Asymptotics.pdf
Mar. 18, 10.15 - 12.00, J3506 Platon. Lecture 4: Least-squares estimation. Method of moments. Chapters 8-9. Lecture 4.pdf Notes from the smart board: WLS_Challenge.pdf
Mar. 25, 10.15 - 12.00, J3506 Platon. Lecture 5: Bayesian estimation. Chapters 10-11. Lecture 5.pdf Derivation of the MMSE formula for the linear Gaussian case.
Apr. 1, 10.15 - 12.00, J3506 Platon. Lecture 6: Linear MMSE estimation. Chapters 12-13. Lecture 6.pdf Complement from smart board.
Apr. 8, 10.15 - 12.00, J3506 Platon. Lecture 7: Detection, Classification and model order selection. Hand-out material: Hero_signal_Detection_and_Classification.pdf On_information_criteria_and_the_generalized_likelihood_ratio_test_of_model_order_selection.pdf Lecture notes: Lecture 7.pdf
Apr. 15, 10.15 - 12.00, J3506 Platon. Lecture 8: Summary and extensions. Chapters 14-15. Lecture notes: Lecture 8.pdf Smart Board Notes: MLE of Covariance Matrix.pdf

Tutorials (content: see lecture notes last page). Can also be followed at: https://bth.zoom.us/j/67282644561?from=addon

Mar. 3, 10.15 - 12.00, J3506 Platon. Tutorial 1. Notes from tutorial session 1. Matlab code: Example 3.5 (corrected April 22, also includes Example 3.7)
Mar. 10, 10.15 - 12.00, J3506 Platon. Tutorial 2. Smart board notes from session 2. Matlab code: Example 4.1.
Mar. 17, 10.15 - 12.00, J3506 Platon. Tutorial 3. Smart board notes from session 3. Matlab code: Example 7.4.
Mar. 24, 10.15 - 12.00, J3506 Platon. Tutorial 4. Smart board notes from session 4. Matlab code: Frequency estimation.
Mar. 31, 10.15 - 12.00, J3506 Platon. Tutorial 5. Smart board notes from session 5. Matlab code: A5_2.mlx
Apr. 7, 10.15 - 12.00, J3506 Platon. Tutorial 6. Smart board notes from session 6. Matlab code: A6_3.mlx A6_4.mlx
Apr. 14, 10.15 - 12.00, J3506 Platon. Tutorial 7. Unfortunately we lost the notes from the Smart board this time. But Wu has provided his solutions to D1 and D2 as well as the Matlab code for D3 and D4.