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.

Requisites

• MIDS students only. DATASCI 207. We assume you are familiar with machine learning techniques. You should also be comfortable with linear algebra, which we'll use for vector representations and when we discuss deep learning. Intermediate programming skills in an object-oriented language (e.g., Python). This course will use Python for all examples, exercises, and assignments.

Repeat Rules

Course is not repeatable for credit.