This course is designed to introduce students to a subset of computer vision that relies on deep learning, spanning both introductory and recent state-of-the-art methods. Our goal is to give students a breadth of understanding of how different computer vision systems can be applied to a wide variety of tasks, as well as a depth of understanding for a certain subset of such systems. Students will ideally leave with:

1. Wide exposure to different systems used for solving different computer vision problems and a high-level understanding of how and why they work
2. An understanding of what these systems like these look like under the hood and how they are translated from high-level ideas to low-level details in practice
3. Experience in implementing end-to-end deep learning systems from scratch in PyTorch

Immediately after completing this course, it is our hope that students will have the knowledge and practical experience necessary to undertake independent projects in the area of computer vision and continue their education on their own.

Every week, there will be two 1 hour live lectures, and an associated concept-check quiz due on the following Monday. Lectures will be held in-person, and attendance is mandatory. In addition, there will be four programming-heavy homework assignments spread across the semester, where students will get an opportunity to implement and interact with the concepts learned in class.

All of the materials, including the lecture videos, slides, and assignments, will be updated on the course website during the progression of the course.

Please fill out the following Google Form. If you get accepted, you will not be automatically enrolled—instead, we will provide you with a permission code to enter on CalCentral.