Semester: Fall 2020. Time and Location: Thursday 7:10-9:00pm EST, Online. See NYU classes for Zoom link. Instructor: Rob Fergus Office hours: Thursday 5:45-6:45pm, In Person + Virtual, Room 312, Warren Weaver Hall.
Course TAs: Hemant Singh (hs3644@nyu.edu), Karanbir Chahal (ksc487@nyu.edu)
Piazza Link: here |
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Computer Vision aims to extract descriptions of the world from pictures or video. In recent years, much progress has been made on this challenging problem. The course will start by looking the established area of geometric vision. It will then introduce the convolutional network model and describe the profound impact that it has had on problems in recognition, segmentation and many other vision problems.
The course will be suitable for master's students and advanced undergraduates. A reasonable knowledge of linear algebra will be required, along with some basic concepts in machine learning. The homeworks will require PyTorch, so familiarity with it is desirable, although not essential.
Assessment will be through three graded homework assignments and a final course project. The weighting across these will be: 17% 17%, 17%, 49%.
The policy regarding late homework is as follows: (a) assignments that are late by less than 24hrs will suffer a 10% reduction; (b) those between 24 and 72 hrs late will suffer a 25% reduction and (c) those more 72hrs late will suffer a 50% reduction. You are strongly encouraged to start the assignment early and don't be afraid to ask for help.
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Szeliski, Ch. 1 and 2; F & P, Ch. 1 |
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Szeliski, Ch. 6; F and P, ch. 3.1 and 15; Lowe 2004 |
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Winder and Brown 2007 |
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Szeliski, Ch. 7; F & P ch. 10 & 11; |
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Szeliski, Ch. 14 |
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We will not be using any textbook in this class.