Data Science 281

Computer Vision

3 units

Course Description

This course provides learners hands-on data management and systems engineering experience using containers, cloud, and Kubernetes ecosystems based on current industry practice. The course will be project-based with an emphasis on how production systems are used at leading technology-focused companies and organizations. During the course, learners will build a body of knowledge around data management, architectural design, developing batch and streaming data pipelines, scheduling, and security around data including access management and auditability. We’ll also cover how these tools are changing the technology landscape.

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

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Hany Farid
Hany Farid
Associate Dean and Head of School; 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:

November 12, 2021