ECE 133A: Applied Numerical Computing

UCLA, Fall 2026

ECE 133A introduces the methods and tools of numerical computing. We will study how core linear algebra concepts are applied to real-world engineering, data science, and machine learning applications.

📌 Course Information

Instructor: Liz Izhikevich (lizhikev@ucla.edu). Office Hours: 10:00–11:00 AM on selected Wednesdays in E4 56-147G. See the calendar below for scheduled office hours, or meet by appointment.

TA: Rafi Zahedi (rafi73@ucla.edu). Office Hours: Mondays, 10:30 AM–12:30 PM on Zoom.

Course Material: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe. The textbook website includes the book and supporting resources; textbook solutions (PDF) are also available. Course videos are available on YouTube. We will aim to cover one chapter per lecture, with all slides found here.

Communication: Students must create an account on Ed Discussion for course discussion and questions.

Submissions: This class has no graded homework, only quizzes and a final project. Students must create a Gradescope account to receive their grades. Entry code: G732N6.

Attendance: For attendance extra credit, enroll in iClicker.

Exams: This class has no in-person final exam or midterm. However, there is a weekly closed-note quiz during discussion section. See Course Structure below for more details.

🗓️ Tentative Topics and Schedule

🚩 Course Structure

Final course grades are based on the following three components:

📝 Quizzes (70%)

Quizzes are intended to ensure that students actually know the material. They will be short and taken during the first 30 minutes of discussion. The best way to prepare is to be familiar with all textbook problems from the relevant chapters. Up to two quiz scores may be dropped.

💬 In-person discussion activities (20%)

Discussion activities are completed in person during Friday discussion. Up to two activity scores may be dropped.

🔬 Final project (10%)

Students will complete a final project applying numerical-computing methods to an engineering problem. Project requirements and milestones will be announced during the quarter.

📍 Attendance (extra credit)

Students who attend at least 10 lectures in person will receive an automatic 2% grade boost. Virtual lectures are not eligible for attendance extra credit.

🤖 AI Policy

Use of large language models (LLMs) is heavily encouraged to help students understand how to solve problems and to generate additional practice problems. AI use is also acceptable for the final project. Students will be quizzed separately about the design choices and methods in their projects to ensure that they deeply understand the work they produced. To ensure students deeply understand the course material, students will also be quizzed on problem solving.

📅 Late Registration Policy

Because of the size of the course, we cannot offer make-up quizzes or retroactive attendance credit. The course policies include built-in flexibility: up to two quiz scores may be dropped, and attendance extra credit requires attendance at 10 in-person lectures. Students who enroll late should use this flexibility for any quizzes or attendance opportunities missed before enrollment. Missed quizzes will receive zeros and count toward the two available quiz drops.