EE263s: Course Information

Lectures & section

Lectures: Tuesdays and Thursdays, 2:45–4pm, Skilling 191. Also available via SCPD.

Textbook and optional references

There is no textbook. Everything we’ll use is posted on this website in pdf format.

Several texts can serve as auxiliary or reference texts:

• Linear Algebra and its Applications, or the newer book Introduction to Linear Algebra, G. Strang.

• Introduction to Dynamic Systems, Luenberger, Wiley.

You really won’t need these books; we list them just in case you want to consult some other references.

Course requirements and grading

Requirements:

• Weekly homework assignments due on Friday. You are allowed, even encouraged, to work on the homework in small groups, but you must write up your own homework to hand in. Homework will be graded on a scale of 1–10.

There will be a short midterm exam, and probably a longer (possibly 24 hour) final exam.

Grading: Homework 12%, midterm 35%, final 40%, participation 13%. These weights are approximate; we reserve the right to change them later.

Prerequisites

Exposure to linear algebra and matrices (as in Math. 103). You should have seen the following topics: matrices and vectors, (introductory) linear algebra; differential equations, Laplace transform, transfer functions. Exposure to topics such as control systems, circuits, signals and systems, or dynamics is not required, but can increase your appreciation.

Catalog description

Applied linear algebra and linear dynamical systems with application 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 MATH 103; differential equations and Laplace transforms as in EE 102A.

2 units. A 3 unit version is typically taught in the Autumn and Spring quarters.