Schoolhouse.world: peer tutoring, for free.
Schoolhouse.world: peer tutoring, for free.
Schoolhouse.world: peer tutoring, for free.
Introduction to Computer Vision

SAT Score Range

1 session

About

This course introduces the basics of computer vision, including how images are represented, color spaces, edge and feature detection, and simple object recognition. Learners will build small projects such as color-based object detectors, edge-detection visualizers, and simple vision pipelines that process real images step by step. By the end of the course, learners will be able to create a mini end-to-end vision system, similar to what’s used in self-driving cars and big-tech AI products, that takes in camera input and makes real decisions from images.


Tutored by

Ishaan A 🇺🇸

Certified in 1 topic

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I’m a student passionate about STEM, especially robotics and engineering. I enjoy building systems that combine software and hardware, from writing control code to testing real-world mechanisms.

✋ ATTENDANCE POLICY

Learners are expected to attend sessions regularly and arrive on time, as each session builds on the previous one. If you know you’ll miss a session, please communicate ahead of time through Schoolhouse; repeated unexplained absences or lack of participation may result in removal from the series so spots can be opened for committed learners.

SESSION 1

25

Dec

SESSION 1

Artificial Intelligence

Artificial Intelligence

Thu 3:30 AM - 4:30 AM UTCDec 25, 3:30 AM - 4:30 AM UTC

In the first 60-minute session, we’ll introduce what computer vision is and how it’s used in real-world systems like robotics and self-driving cars. We’ll break down how computers see images as pixels and colors, then walk through a simple vision pipeline such as detecting an object based on color. The session will include a short interactive demo and end with a preview of more advanced detection techniques coming next.

Public Discussion

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Dec 25

1 week

60 mins

/ session

Next session on December 25, 2025

SCHEDULE

Thursday, Dec 25

3:30AM