Introduction to Computer Vision guides learners through the essential algorithms and methods to help computers 'see' and interpret visual data. You will first learn the core concepts and techniques that have been traditionally used to analyze images. Then, you will learn modern deep learning methods, such as neural networks and specific models designed for image recognition, and how it can be used to perform more complex tasks like object detection and image segmentation. Additionally, you will learn the creation and impact of AI-generated images and videos, exploring the ethical considerations of such technology.
This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:
MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
Status: Digital Signal Processing
Digital Signal Processing
Status: Linear Algebra
Linear Algebra
Beginner·Course·16 hours
Featured reviews
4.0
·Reviewed Feb 21, 2026
The course was nice and easy until the last module where some lectures were presented in a very confused way.
5.0
·Reviewed Jun 17, 2026
Professor Yeh is incredible. Presents information in a way where you find the intuition yourself.
5.0
·Reviewed Jun 8, 2026
Enjoyed the course. Seeing the math worked through step by step in Excel was extremely beneficial.
4.0
·Reviewed Aug 17, 2026
A little more explanation on Module 4 would have been helpful
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J
Jason
4.0
·Reviewed Jun 26, 2026
While this is a useful workthrough of how computer vision computation works, the assessments are mismatched to course content: many questions are related to material never directly covered in the course.
G
Giuseppe
4.0
·Reviewed Feb 21, 2026
The course was nice and easy until the last module where some lectures were presented in a very confused way.
C
Carson
5.0
·Reviewed Oct 15, 2024
Professor Tom Yeh has proven that the success of his Gen AI course was not a fluke. With clear explanations, no busy work, and straight forward practical examples, Prof Yeh stands out as one of the best Universities have to offer.
J
John
5.0
·Reviewed Jun 9, 2026
Enjoyed the course. Seeing the math worked through step by step in Excel was extremely beneficial.
Z
Zac
5.0
·Reviewed Jun 17, 2026
Professor Yeh is incredible. Presents information in a way where you find the intuition yourself.
W
wonseok
5.0
·Reviewed Oct 20, 2024
fantastic
A
Abror
5.0
·Reviewed Dec 9, 2024
perfect
자
자연과학계열/노승준
5.0
·Reviewed Jul 30, 2026
good
A
Anthony
4.0
·Reviewed Aug 18, 2026
A little more explanation on Module 4 would have been helpful
I
Issa
4.0
·Reviewed Dec 24, 2025
the last
V
Vikas
4.0
·Reviewed Mar 10, 2026
good
J
Jing
3.0
·Reviewed Apr 21, 2026
- The Filter2D learner workbook is not updated to the version used in the lecture videos.
- His lecture is very mechanical. It’s useful to go through the steps of how to do a thing such as dilating an image. However, he never seems to explain the concepts or what their applications are. This makes his lectures less meaningful.
- His way of explaining things is not clear, not precise, and not organized.
- Incorrect spelling such as for “epipolar”, he wrote “epiplor” in one of his lectures.