## EE103: Course Information## LecturesTuesdays and Thursdays, 9am–10:20am, Bishop Auditorium. ## SectionsThe sections will meet weekly starting the week of October 3. The sections will be 2 hours long, with the first hour or so spent on covering applications, and the remaining time used as office hours. Mark Nishimura: Tuesday, 2:30-4:30pm at Littlefield Center 103 (L103) Enzo Busseti: Tuesday, 3:30-5:30pm at 260-004 Vikram Prasad: Tuesday, 4:30-6:30pm at GESB 134 Guillermo Angeris: Tuesday, 6:00-8:00pm at 260-004 Reese Pathak: Tuesday, 7:00-9:00pm at 260-003 Extra Section (staffed on a rotating basis): Wednesday, 10:30am-12:30pm at 420-050 Pin Pin Tea-Mangkornpan: Wednesday, 1:30-3:30pm at ART 360 Do-Hyoung Park: Wednesday, 3:30-5:30pm at 380-381T Richard Mu: Wednesday, 6:00-8:00pm at 380-381U Jihee Hwang: Wednesday, 7:00-9:00pm at 260-004 Karen Leung: Thursday, 1:30-3:30pm at 260-004 Lin Fan: Thursday, 3:00-5:00pm at 200-303
## 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 in the box accross from Packard 243.**Late homework will not be accepted.***Midterm exam*. The miderm will be in class, Thursday October 27, 9am–10:20am.*Final exam*. The final exam is scheduled for Wednesday December 14, 8:30am–11:30am.
## 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. |