Data Science 281

Computer Vision

3 units

Course Description

This course introduces the theoretical and practical aspects of computer vision, covering both classical and state of the art deep-learning based approaches. This course covers everything from the basics of the image formation process in digital cameras and biological systems, through a mathematical and practical treatment of basic image processing, space/frequency representations, classical computer vision techniques for making 3-D measurements from images, and modern deep-learning based techniques for image classification and recognition.

Student Learning Outcomes

  • Be able to read and understand research papers in the computer-vision literature.

  • Build computer vision systems to solve real-world problems.

  • Properly formulate problems with the appropriate mathematical and computational tools.

  • Understand the building blocks of classical computer vision techniques.

  • Understand the building blocks of modern computer vision techniques (primarily artificial neural networks).

  • Understand the process by which images are formed and represented.

Course Designers

Profile profile for hfarid

Hany Farid
Hany Farid
Professor (I School and EECS)
203A South Hall

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Shruti Agarwal
Shruti Agarwal
Postdoctoral Scholar Lecturer

Prerequisites

DATASCI 207. MIDS students only. You should also be comfortable with linear algebra, which we'll use for vector representations and when we discuss deep learning. This course will use Python for all examples, exercises, and assignments.

Last updated:

December 13, 2021