Computational Vision
Explore human and machine vision models, from edge detection to neural networks for visual processing and recognition.
Explore human and machine vision models, from edge detection to neural networks for visual processing and recognition.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Mind and Machine Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.4
(62 ratings)
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What you'll learn
Apply and analyze human and machine vision models
Understand the geon model for object recognition
Evaluate mental imagery theories and debates
Explore neural network architectures for vision
Analyze deep learning approaches to visual processing
Skills you'll gain
This course includes:
5.7 Hours PreRecorded video
5 assignments
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FullTime access
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There are 4 modules in this course
This comprehensive course explores computational vision from both human and machine perspectives. Students learn about fundamental vision models, including edge detection, depth perception, and object recognition using the geon model. The curriculum covers mental imagery debates and neural network architectures, from simple perceptrons to modern deep learning approaches, providing insights into how both biological and artificial systems process visual information.
Introduction
Module 1 · 38 Minutes to complete
Edges, Depth, and Objects
Module 2 · 2 Hours to complete
Mental Imagery
Module 3 · 2 Hours to complete
Machine Learning and Neural Networks
Module 4 · 2 Hours to complete
Fee Structure
Instructor
Research Associate
Dr. David Quigley is a Research Associate in the Institute of Cognitive Science and an Assistant Professor - Adjunct in the Department of Computer Science at the University of Colorado Boulder. His research focuses on applying learning analytics techniques to develop machine learning models that analyze student activity and understanding in science classrooms. Dr. Quigley earned his Ph.D. from CU Boulder, where he contributed to projects such as the Inquiry Hub Research-Practice Partnership and the Chicago City of Learning initiative.Prior to his doctoral studies, Dr. Quigley completed his undergraduate and master’s degrees at Georgia Tech, working with the Contextual Computing Group on various projects related to human-computer interaction and educational technology.At CU Boulder, he teaches courses including "Computational Vision," "Interpersonal, Developmental, and Evolutionary Perspectives of the Mind," "Methods for Solving Problems," and "What is 'the mind' and what is artificial intelligence?" His work not only enhances educational practices but also contributes to a deeper understanding of how technology can support learning in science education.Dr. Quigley's contributions to the field of cognitive science and education technology position him as a key figure in advancing research on learning analytics and its practical applications in classroom settings.
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4.4 course rating
62 ratings
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