EE103: Course Information

Lectures

Tuesdays and Thursdays, 9am–10:20am, Hewlett 200.

Sections

The 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.

  • Mark Nishimura: Tuesday 2:30pm - 4:30pm, in Herrin T195.

  • Trisha Jani: Tuesdays, 3:30pm - 5:30pm, in Littlefield 103 (L103).

  • John Sholar: Tuesdays, 6:00pm - 8:00pm, in Herrin T195.

  • Sean Chang: Tuesdays, 7:00pm - 9:00pm, in Herrin T185.

  • Reese Pathak: Wednesdays, 10:30am - 12:30pm, in STLC 118.

  • Logan Spear: Wednesdays, 2:30pm - 4:30pm, in STLC 118.

  • Neal Patel: Wednesdays, 3:30pm - 5:30pm, in Herrin T195.

  • Caitlin Go: Wednesdays, 6:00pm - 8:00pm, in Herrin T195.

  • Juliet Daniel: Wednesdays, 7:00pm - 9:00pm, in Hewlett 103.

  • Lucy Li: Thursdays, 10:30am - 12:30pm, in Green Earth Sciences Building 150 (GESB 150).

  • Jonathan Lin: Thursdays, 2:30pm - 4:30pm, in Herrin T195.

  • Anthony Degleris: Thursdays, 3:30pm - 5:30pm, in Herrin T185.

  • Sofia Jimenez: Thursdays, 6:00pm - 8:00pm, in Herrin T185.

  • Guillermo Angeris: Thursdays, 7:00pm - 9:00pm, in Herrin T195.

Office hours

Stephen Boyd's office hours: Tuesdays 10:20am–11:50pm, Packard 254.

David Tse's office hours: Thursdays 1:00pm - 3:00pm, Lathrop 282 (alongside homework party).

Homework party: Thursdays 1:00pm - 5:00pm, Lathrop 282. The homework parties will be supervised by David Tse and Govinda Kamath. The first homework party will be held on Thursday, 10/5.

TA office hours: The TAs’ office hours will be folded into the last hour (or so) of the weekly sections.

Textbook

The textbook is written by Stephen Boyd and Lieven Vandenberghe, and is available on-line. You will not need to consult any other books or materials (though you are welcome to do so).

Course requirements and grading

Requirements:

  • 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.

Grading: Attendance/participation 5%, homework 30%, midterm exam 25%, final exam 40%.

Prerequisites

You 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 very simple programming in the language Julia, so you should have seen some very basic simple programming. CS106A or equivalent (which is more than you will need) is a prerequisite or corequisite. You do not need to know about any applications; we'll cover that in detail. Even if you have already seen all the material in the course (e.g., vectors, matrices, least squares) we encourage you to take it, because (we guess) you haven't seen it the way we will present it.

Syllabus

Our goal is to cover the textbook chapters 1–18 (Constrained least squares applications) during the course.

Quizzes

This class has no formal quizzes. There are on-line quizzes on the lecture slides page. These are just for fun; they are not graded and your responses are not logged.

Catalog description

Introduction 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 Ways of Thinking (Formal Reasoning) course.

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 objectives

The 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 all around you, and how it is used in zillions of applications. You'll learn some basic machine learning, portfolio optimization and finance, audio and image processing, and other applications.

Intended audience

The course is targeted at undergraduate students in all fields, just as CS106a is. As long as you've got the prerequisites, and are up for it, you're welcome.