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Course Description

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.

General Information

Class Time and Location


Tuesday/Thursday, 3:00 - 4:20PM
@Building 320, Rm 105


Building 300, 300

*See syllabus for dates


Link to details on when assignments are due and what will be taught every day.


Recommended but not required: Computer Vision: Algorithms and Applications, 2nd ed.
Richard Szeliski

Free PDF Download


Details on how to work on and submit each assignment.

Office Hours

Juan Carlos Niebles

@Google Meet

Book in advance

Adrien Gaidon

In person after class +

Book in advance

Johnny Chang

10:00AM - 12:00PM
12:00PM - 2:00PM @QueueStatus

Alina Chou

5:00PM - 7:00PM
5:00PM - 7:00PM @QueueStatus

Carolyn Qu

12:00PM - 2:00PM Friday
12:00PM - 2:00PM @QueueStatus

Micael Tchapmi

2:00PM - 4:00PM Friday
2:00PM - 4:00PM @QueueStatus

Honglin Chen

6:00PM - 8:00PM @QueueStatus

Sasha Moore

Tuesday (virtual)
10:00PM - 12:00PM @QueueStatus

Websites you should sign up for.

Course Discussions

Please use Ed to ask questions you have throughout the course.


Submit your assignment notebooks and PDFs to Gradescope. The email associated with your Canvas account will be automatically added.

Grading Policy

Summary Deliverables will be due on Fridays at 11:59pm (PT)

Late policy

You will have a total of 7 late days that you can use in whichever assignments you prefer. There is a limit of 3 late days used per assignment, which means that the hard deadline for each assignment is on Monday at 11:59pm. Homeworks will still be accepted after your 7 late days have been used, but a 25% penalty will be applied for each additional late day.


Q: Who should I contact for OAE letter and request?

A: For OAE letters and requests, please email our head TA Johnny Chang.


Proficiency in Python (NumPy)

All class assignments will be in Python (with numpy.) Please review this NumPy tutorial to help with your assignments.

Linear Algebra (e.g. MATH 51)

We will use matrix transpose, inverse, rotation, translation and other algebraic operations with matrix expressions. If you are a quick learner you should be able to learn them during the class if you haven’t yet. We will have review sessions and provide review materials.

Calculus (e.g. MATH 19 or 41)

You’ll need to be able to take a derivative, and maximize a function by finding where the derivative=0.

Probability and Statistics (e.g. CS 109)

You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc.

Course Calendar