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 techniques. 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. A wide exposure to a plethora of systems used to solve different computer vision problems and a high-level understanding of how and why they work.
2. An understanding of what these systems 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 or pre-recorded videos, and an associated concept-check quiz due on the following Monday. 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 class website during the progression of the course.

We will consider the responses in the application below. While we would love to enroll everyone, we are unfortunately limited by room capacity.