In this course, we will expand on vision as a cognitive problem space and explore models that address various vision tasks. We will then explore how the boundaries of these problems lead to a more complex analysis of the mind and the brain and how these explorations lead to more complex computational models of understanding.
Computational Vision
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Computational Vision
This course is part of Mind and Machine Specialization
Instructor: David Quigley
4,955 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.4
(63 reviews)
Beginner level
No prior experience required
8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Apply various models of human and machine vision and discuss their limitations.
Demonstrate the geon model of object recognition and its limitations.
Argue the benefits and drawbacks of the symbolist and visualist perspectives of mental imagery.
Recognize the single layer and multi-layer perceptron neural network models of artificial intelligence.
Details to know
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Assessments
5 assignments
Taught in English
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This course is part of the Mind and Machine Specialization
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There are 4 modules in this course
This week we will explore some basic assumptions of a simple model of human vision.
What's included
1 video1 reading1 assignment
This week we will explore models of higher-order tasks solved by the visual system.
What's included
3 videos2 assignments3 discussion prompts
This week we will compare and contrast different perspectives of how mental imagery relates to the visual system.
What's included
2 videos1 reading1 assignment
This week we will explore the neuron as an element of the human cognitive system and ways we can implement these pieces into neural network systems of artificial intelligence.
What's included
3 videos1 reading1 assignment
Instructor
Instructor ratings
4.7 (15 ratings)Offered by
Recommended if you're interested in Algorithms
California Institute of the Arts
MathWorks
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Google Cloud
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