CSE 455: Computer Vision

Class: M/W 1:30-2:50pm, BAG 154

Recitation: Fri 1:30-2:20pm, BAG 154

About the course

Ever wonder how robots can navigate space and perform duties, how search engines can index billions of images and videos, how algorithms can diagnose medical images for diseases, how self-driving cars can see and drive safely or how instagram creates filters or snapchat creates masks? In this class, we will explore all of these technologies and learn to prototype them. Lying in the heart of these modern AI applications are computer vision technologies that can perceive, understand and reconstruct the complex visual world. Computer Vision is one of the fastest growing and most exciting AI disciplines in today’s academia and industry. This 10-week course is designed to open the doors for students who are interested in learning about the fundamental principles and important applications of computer vision. We will expose students to a number of real-world applications that are important to our daily lives. More importantly, we will guide students through a series of well designed projects such that they will get to implement a few interesting and cutting-edge computer vision algorithms.

Important Links

Canvas: Canvas

Gradescope: Gradescope

EdStem: Edstem

Course Staff + Office Hours

We will not be hosting office hours for the first week

Instructors	Teaching Assistants

Ruta Desai	Chun-Liang Li	Ayush Agrawal	Joshua Jung	Jun Wang	Nishat Khan	Raymond Yu	Simran Bagaria
Hours: Fri	Hours: TBD	Hours: Th	Hours: Tue	Hours: Fri	Hours: Wed	Hours: Mon	Hours: Mon
10am-12pm	TBD	5:30pm-7:30pm	4:30pm-6:30pm	4:30pm-6:30pm	12:30pm-2:30pm	3:30pm-5:30pm	11pm-1pm
Zoom	TBD	Zoom	CSE1 220	CSE2 153	CSE1 218	CSE2 151	CSE1 2nd Floor Breakout
rutapd@cs. washington.edu	chunlial@cs. washington.edu	ayush123@cs. washington.edu	jjung04@cs. washington.edu	junw3@cs. washington.edu	nkhan51@cs. washington.edu	ryu5@cs. washington.edu	sbagaria@cs. washington.edu

Prerequisites

Linear Algebra, Calculus and Statistics. While it is recommended to have some prior background in Machine Learning, the necessary fundamentals will be covered as part of this class.

Course format

The class format will be a combination of lectures, 5 assignments, and a final exam.