CSE 455: Computer Vision

Class: T/Th 10:00-11:20am, CSE2 G20

Recitation option 1: Fri 9:30-10:20pm, MGH 231

Recitation option 2: Fri 12:30-1:20pm, CSE2 G01

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: https://canvas.uw.edu/courses/1786126/

Gradescope: https://www.gradescope.com/courses/942464 (Code: Z3EXZY)

EdStem: https://edstem.org/us/courses/70815

Course Staff + Office Hours

Instructors							Guest Lecturer

Ranjay Krishna	Ayush Agrawal	Simran Bagaria	Joshua Saejune Jung	Nishat Khan	Rustin Soraki	Raymond Yu	Jieyu Zhang
Hours: Tue	Hours: Mon	Hours: Mon, Tue	Hours: Tue	Hours: Wed	Hours: Thu, Fri	Hours: Thu	Hours:
1-2pm	10:30am-12:30pm	Mon: 2:30-4pm, Tue: 3-4pm	4:30-6:30pm	12:30-2:30pm	Thu: 11am-12pm, Fri: 2-3:30pm	3-5pm	TBD
CSE2 304	CSE2 131	CSE2 387	CSE1 220	CSE1 218	CSE1 482	CSE2 153	TBD
ranjay@cs. washington.edu	ayush123@cs. washington.edu	sbagaria@cs. washington.edu	jjung04@cs. washington.edu	nkhan51@cs. washington.edu	rustin@cs. washington.edu	ryu5@cs. washington.edu	jieyuz2@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.