## EE263: Course infoProfessor Sanjay Lall,
Stanford University,
Autumn Quarter 2013–14
## Lectures & review sessions
## Textbook and optional referencesThere is no textbook.
Everything we'll use is posted on the 263 website in pdf format.
The course reader, which is nothing but a collection of
all the material on this website, 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
Weekly homework assignments. Homework will normally be assigned each Thursday and due the following **Thursday by 7 pm**in the cabinet inbox near Packard 243.**Late homework will not be accepted.**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 roughly, with each problem being worth four points.Midterm exam (24 hour take home), scheduled for Nov. 5–6, Nov 6–7, Nov 7–8 or Nov 8–9 (your choice). Final exam (24 hour take home), scheduled for Dec 6–7, Dec 7–8, Dec 8–9, Dec 9–10 or Dec 10–11 (your choice).
## PrerequisitesExposure to linear algebra and matrices (as in Math 104). 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 descriptionIntroduction to applied linear algebra and linear dynamical systems, with applications to circuits, signal processing, communications, and control systems. Topics include: Least-squares aproximations of over-determined equations and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm and singular value decomposition. Eigenvalues, left and right eigenvectors, and dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input multi-output systems, impulse and step matrices; convolution and transfer matrix descriptions. Control, reachability, state transfer, and least-norm inputs. Observability and least-squares state estimation. EE263 covers some of the same topics, but is complementary to, CME200. |