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.
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.
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.
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.