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.
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이 강좌에 대하여
배울 내용
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.
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콜로라도 대학교 볼더 캠퍼스
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
강의 계획 - 이 강좌에서 배울 내용
Introduction
This week we will explore some basic assumptions of a simple model of human vision.
Edges, Depth, and Objects
This week we will explore models of higher-order tasks solved by the visual system.
Mental Imagery
This week we will compare and contrast different perspectives of how mental imagery relates to the visual system.
Machine Learning and Neural Networks
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.
Mind and Machine 특화 과정 정보
This specialization examines the ways in which our current understanding of human thinking is both illuminated and challenged by the evolving techniques and ideas of artificial intelligence and computer science. Our collective understanding of “minds” – both biological and computational – has been revolutionized over the past half-century by themes originating in fields like cognitive psychology, machine learning, neuroscience, evolutionary psychology, and game theory, among others. This specialization focuses on both the larger “historical” arc of these changes as well as current research directions and controversies.

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