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Speaker: Keith Merril
(Fri Oct 25, lecture 15)
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Semi-mathish introduction to Kalman filters and related matters

#### Homework due for today

**Legend**: : Participation (pass/fail) | : PDF | : Team | : Attachment

- Read How a Kalman Filter Works in Pictures. Respond to the following warmup questions:
- Would you guess that a covariance matrix is symmetric? What’s your reasoning?
- In the example with the robot in the forest there’s an (inaccurate) GPS and an (inaccurate) prediction of travel. How good an estimate of position would you get if the robot didn’t move at all?
*Deliverable:* Your answer to these questions in a pdf.

##### Guest Lecture

We are lucky today to have Professor Keith Merrill of the Brandeis Mathematics Department. Keith happens to be one of the most popular Math professors at Brandeis. His research lies at the intersection of number theory, dynamics, and geometry; specifically the field of Diophantine approximation [Ed: HUH???].

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