Preliminary Lecture Plan

The times for the lectures are preliminary and up for discussion to minimize overlap with other courses.

Slides will be linked from the lecture number in advance.

Recomended reading refers to the textbook Linear Estimation.

Nr.	When	Where	Content	Slides	Reading
1.	March 9 2026 10-12	Visionen, Large Conference Room	Least-squares (LS) estimation and the conditional mean. Linear LS estimation and the special case of Gaussian random variables.	1-up 4-up notes	Sec. 3.1-3.2.4 article [1]
2.	March 30 2026 15-17	Nollstället	Geometric interpretation of linear LS estimation. Spectral factorization. Discrete time causal Wienerfilters.	1-up 4-up notes	Sec. 3.3 (Bonus: Sec. 3.4-3.5) Sec. 6.3-6.5 Sec. 7.3-7.7
3.	April 13 2026 13-15	Visionen, Large Conference Room	Bayes: Estimating an point or a density. State-space models and Markov process. Recursive Bayesian filtering. Linear Gaussian models and the Kalman filter.	notes	Lecture notes only
4.	April 28 2026 15-17	Visionen, Large Conference Room	The innovation process. The Kalman filter from an innovation process perspective.	notes	Sec. 4.1-4.2.4 Sec. 9.1-9.4
5.	May 12 2026 8-10	Nollstället	Observability and controllability. Time Invariance of the Kalman Filter. Frequency Domain Expressions.	notes	Sec. 1.5 Sec. 14.1-14.3
6.	June 1 2026 10-12	Visionen, Large Conference Room	Smoothing filters. Information Form Kalman Filter. Extended Kalman Filter (EKF).	notes	Sec. 9.5-9.9 Sec. 10.1-10.4