[image] [credit]

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.

Instructors

Juan Carlos Niebles
Adrien Gaidon
Silvio Savarese

Teaching Assistants

Devin Gupta
Ishan Khare
George Birikorang
Gabriela Dias

General Information


Class Time and Location

 Lectures

Monday / Wednesday
3:00 - 4:20PM
@Hewlett Teaching Center, Rm 201

Recitations*

TBD

*See syllabus for dates


Syllabus

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


Textbook

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


Assignments

Details on how to work on and submit each assignment.

Office Hours

Juan Carlos Niebles

In person after class


Adrien Gaidon

In person after class

Silvio Savarese

In person after class

Devin Gupta

Tuesdays 1:30 pm - 3:30pm
@Zoom

Ishan Khare

TBD
@Zoom

George Birikorang

TBD
@Zoom

Gabriela Dias

TBD
@Zoom

Important Websites


Course Discussions

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


Class notes

Here is the link to the repository of past notes.


Gradescope

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

Grading Policy

Assessment Item Weight
Homework 0 (basics) 10%
Homework 1 10%
Mini-Project 1 15%
Homework 2 10%
Mini-Project 2 15%
Final Project: Proposal 5%
Final Project: Demo Day + Report 35%
Deliverables will be due on Wednesdays 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 Saturday 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.

FAQ

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

A: For OAE letters and requests, please email the TA George Birikorang.

Prerequisites


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.