Logistics:

Grade: The grade will be 80% homework assigments and 20% a final project to be done in groups of 2 or 3.

Project: The projects will be due on Monday December 11th at midnight and there will be no penalty-free days for it.

Canvas: Here is the link. We will use for submitting assignments, discussion boards and posting lecture slides.

Homework assignments:

In parenthesis, the fraction of the HW grade for each assignment.

• HW0: fun with images (1/9)
• HW1: image filtering, hybrid images (1/9)
• Hw2: feature detection and matching, panoramas (2/9)
• HW3: optical flow (2/9)
• HW4: neural networks from scratch (2/9)
• HW5: PyTorch (1/9)

Note: homework assignments will be due at 11:59pm of their due date. Four penalty-free late days are allowed throughout the quarter on homework assignments. A 10% penalty will be applied for every late day after.

Lectures:

Lecture slides are available on Canvas.

Date	Lecture	Other
September 27	Introduction
October 2	Human Vision, Color Spaces and Transforms	HW0 assigned
October 4	Image coordinates, resizing
October 9	Filters, convolutions	HW0 due, HW1 assigned
October 11	Edges and features
October 16	Harris, matching, RANSAC	HW1 due, HW2 assigned
October 18	HOG, SIFT
October 23	Optical Flow
October 25	3D, Depth Perception, and Stereo
October 30	Machine Learning	HW2 due, HW3 assigned
November 1	More Machine Learning for Computer Vision
November 6	Neural Networks
November 8	Convolutional Neural Networks
November 13	Network Architectures	HW3 due, HW4 assigned
November 15	Semantic Segmentation
November 20	Guest Lecture: TBD
November 22	Thanksgiving, no class.
November 27	Object Detection	HW4 due, HW5 assigned
November 29	Detection and Instance Segmentation
December 4	Vision and Language	HW5 due
December 6	Generative Adversarial Networks