EE263: Introduction to Linear Dynamical Systems

Sanjay Lall, Stanford University, Autumn Quarter 2021


Professor Sanjay Lall


  • Mondays and Wednesdays, 9:45AM - 11:15AM

  • First lecture 9/20, last lecture 12/1

  • location: nVidia Auditorium

This class will be taught live. All lectures will also be recorded (by SCPD) and posted on Canvas approximately one hour after the class ends. The lectures will also be viewable remotely on Canvas and on Zoom, with zoom ID 999 8757 5980.

Check Ed for the Zoom password.


Stanford has covid information for students, and information on classroom safety. You are required to follow these Stanford policies on mask wearing, testing, and vaccination in order to attend class and office hours. Do not attend lectures or office hours in person if you have symptoms. If you are unable to attend class or complete classwork due to illness or isolation requirements, additional time is available to complete coursework.

We, the ee263 staff, understand that during the pandemic many of you may not wish to attend class in person, and you should in that case feel free to watch class and attend office hours online. At least 50% of office hours will be online. We are following closely the Stanford protocols to ensure everyone's safety. If you have any concerns, please let us know. These policies will be updated as circumstances change.

Finally, to facilitate participation in remote office hours, students can borrow equipment (including laptops and iPads) from the Lathrop Tech Desk. If you are in isolation, they will deliver the equipment to your room.

Course description

Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: least-squares approximations of over-determined equations, and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singular-value decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input/multi-output systems, impulse and step matrices; convolution and transfer-matrix descriptions. Control, reachability, and state transfer; observability and least-squares state estimation.

Prerequisites: linear algebra and matrices as in MATH104 or Engr108; differential equations as in EE102A.


There are no required or optional textbooks. Complete notes will be available online. See the section on reading for details.


This course was originally developed and taught by Professor Stephen Boyd, and the complete set of materials consisting of lecture videos, slides, support notes and homework is still available in the archive.