Class Time and LocationLectures
Tuesday/Thursday, 2:30 - 3:50PM
Friday, 12:30 - 1:20PM
Link to details on when assignments are due and what will be taught every day.
Recommended but not required: Computer Vision: A Modern Approach by Forsyth & Ponce
Details on how to work on and submit each assignment.
Juan Carlos Niebles
4 - 5PM
11AM - 12PM
Tuesday, Oct. 13
Tuesday, Oct. 20
Tuesday, Nov. 17
10 - 11AM @Zoom
4 - 7PM @Zoom
9AM - 12PM @Zoom
4:30 - 7:30PM @Zoom
9AM - 12PM @Zoom
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.Final
There will be no final exam this year. This exam (20%) has been replaced with the «Lecture Questions» and «Lecture Notes» components of your grade — details on these will be announced shortly.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.
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
You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc.