CSE 455: Computer Vision

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

Recitation: Fri 12:30-1:20pm, JHN 102

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/1718581/

Gradescope: https://www.gradescope.com/courses/755852 (Code: 6G2NBR)

EdStem: https://edstem.org/us/courses/57280

Course Staff + Office Hours

Instructors

Ranjay Krishna	Jieyu Zhang	Mahtab Bigverdi	Xiaojuan Wang	Vivek Jayaram	Zihan Wang	Fatemeh Ghezloo	Minh Hoang
Hours: Tue 9-10am and	Hours: Tue	Hours: Mon	Hours: Fri	Hours: Thu	Hours: Mon	Hours: Wed	Hours: Tue and Thu
Thu 1:30-2:30pm	1-3pm	9-11am	1-2pm, 3-4pm	2-4pm	1-2pm, 4-5pm	9-11am	6-7pm
CSE2 304	CSE2 326	CSE 482	CSE2 275	CSE2 287	CSE2 374	CSE 4th floor breakout	Zoom link (to be posted)
ranjay@cs. washington.edu	jieyuz2@cs. washington.edu	mahtab@cs. washington.edu	xiaojwan@cs. washington.edu	vjayaram@cs. washington.edu	avinwang@cs. washington.edu	fghezloo@cs. washington.edu	minh257@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.