Class Time and Location
Lectures
Tuesday/Thursday, 1:30 - 3:00PM
@Hewlett 201, also Zoom
Friday, 12:15 - 1:15PM
@Building 200-230, also Zoom
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
PDF Draft 28-Aug-2021
Assignments
Details on how to work on and submit each assignment.
Wednesday
9AM - 12PM
@Nooks
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.
Assignment 0 is a simple assignment to get you acquainted with Python and basic libraries we will be using in the course. Each assignment (1 through 8) will be worth 9% each. You will have one week to complete every assignment but all the assignments will be available 2 weeks before they are due. It will be due on Fridays at 11:59pm.
FinalThere will be no final exam this year. This exam (20%) has been replaced with the «Demo Day» and «Lecture Notes» components of your grade — details on these will be announced shortly.
Late policyYou 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.
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.
