Ementorhub

Computational Vision

This course is part of Mind and Machine Specialization

Instructor: David Quigley

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.

Skills you'll gain

Computational Thinking

Psychology

Artificial Neural Networks

Computer Vision

Artificial Intelligence and Machine Learning (AI/ML)

Deep Learning

Image Analysis

Computer Graphics

There are 4 modules in this course

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.

Edges, Depth, and Objects

Mental Imagery

Machine Learning and Neural Networks

Explore more from Algorithms

Visual Perception and the Brain

First Principles of Computer Vision

Computer Vision for Engineering and Science

Machine Learning for Computer Vision