## EE103: Course Information## LecturesTuesdays and Thursdays, 9am–10:20am, Hewlett 200. ## SectionsThe sections will meet weekly starting the week of October 2. Sections will be 2 hours long, with the first hour spent on problem solving and Julia programming, and the remaining time used as office hours. Locations for sections will be posted shortly. Mark Nishimura: Tuesday 2:30pm - 4:30pm Trisha Jani: Tuesdays, 3:30pm - 5:30pm John Sholar: Tuesdays, 6:00pm - 8:00pm Sean Chang: Tuesdays, 7:00pm - 9:00pm Reese Pathak: Wednesdays, 10:30am - 12:30pm Logan Spear: Wednesdays, 2:30pm - 4:30pm Neal Patel: Wednesdays, 3:30pm - 5:30pm Caitlin Go: Wednesdays, 6:00pm - 8:00pm Juliet Daniel: Wednesdays, 7:00pm - 9:00pm Lucy Li: Thursdays, 10:30am - 12:30pm Jonathan Lin: Thursdays, 2:30pm - 4:30pm Anthony Degleris: Thursdays, 3:30pm - 5:30pm Sofia Jimenez: Thursdays, 6:00pm - 8:00pm Guillermo Angeris: Thursdays, 7:00pm - 9:00pm
## Office hours
## TextbookThe textbook is a ## Course requirements and grading
*Attendance at lectures.**Attendance and participation at sections.**Weekly homework assignments*. Homework will normally be assigned each Friday, and due the following Friday by 5pm.**Late homework will not be accepted.***Midterm exam*. The miderm will be in class, Tuesday October 24, 9am–10:20am.*Final exam*. The final exam is scheduled for Wednesday December 13, 8am–11am.
## PrerequisitesYou do not need to have seen any linear algebra before; we will develop
it from scratch. Math 51 is nominally a prerequisite, but we will use
very little of this material.
In the course you'll do some ## Syllabus## Vectors
## Linear independence
## Matrices
## Matrix multiplication
## Matrix inverses
## Least-squares
## Multi-objective least-squares
## Equality constrained least-squares
## Catalog descriptionIntroduction to applied linear algebra with emphasis on applications. Vectors, norm, and angle; linear independence and orthonormal sets; applications to document analysis. Clustering and the k-means algorithm. Matrices, left and right inverses, QR factorization. Least-squares and model fitting, regularization and cross-validation. Constrained and nonlinear least-squares. Applications include time-series prediction, tomography, optimal control, and portfolio optimization. Prerequisites: MATH 51 or CME 100, and basic knowledge of computing (CS 106A is more than enough, and can be taken concurrently). EE103 is part of the EE and MS&E core requirements, approved for
the CS BS Math Elective and the Mathematics & Statistics requirement
in the School of Engineering,
and certified as a EE103/CME103 and Math 104 cover complementary topics in applied linear algebra. The focus of EE103 is on a few linear algebra concepts, and many applications; the focus of Math 104 is on algorithms and concepts. ## Course objectivesThe goal of this course is to introduce you to the basic ideas of
vectors, matrices, and (very basic) linear algebra, emphasizing
applications. We hope that you'll learn how linear algebra is
## Intended audienceThe course will ultimately be targeted at undergraduate students in all fields, just as CS106a is. But for now (until we shake the course out) we are targeting students with a little more background in math, CS, and related areas. |