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CS 776: Deep Learning for Computer Vision

Prerequisites: No formal prerequisite but knowledge of basic probability, calculus and linear algebra is required. 

The course will make the students familiar with basics of learning-based as well as geometric computer vision. The list of possible topics will be

  1. Convolutional Neural Networks
  2. AutoEncoders
  3. Recurrent Neural Networks 
  4. Generative Adversarial Networks
  5. Camera calibration
  6. Epipolar geometry
  7. Structure from motion

This list will evolve based on the level of students enrolled and their interests. For each of the topics we will start with the basics, touch upon some current applications and then you would be expected to work on an assignment which would have a strong programming component. The course is expected to give you a good foundation if you would like to work on Computer Vision in the future either in academic or industrial research and development.