Course Summary
This course introduces the fundamentals of computational vision and explores the intersection between the human brain and artificial intelligence. Students will learn about machine learning, computer vision, and neural networks through lectures and hands-on projects.Key Learning Points
- Gain a fundamental understanding of computer vision and machine learning
- Learn about the intersection between artificial intelligence and the human brain
- Develop practical skills through hands-on projects and assignments
Related Topics for further study
Learning Outcomes
- Understand the principles of computational vision and its applications
- Develop practical skills in machine learning and computer vision
- Identify potential applications of computational vision in various fields
Prerequisites or good to have knowledge before taking this course
- Familiarity with programming in Python
- Basic knowledge of linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Deep Learning
- Applied Data Science
Related Education Paths
Notable People in This Field
- Yann LeCun
- Fei-Fei Li
Related Books
Description
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.
Knowledge
- 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.
Outline
- Introduction
- Vision as a Computational Problem
- Vision by Man and Machine
- Vision Overview
- Edges, Depth, and Objects
- Finding Edges
- Depth Perception
- Object Recognition
- Edges
- Geons
- Mental Imagery
- Mental Imagery and the Brain
- Mental Imagery and the "Turn Towards Neuroscience"
- Mental Imagery and the Visual System
- Mental Imagery
- Machine Learning and Neural Networks
- Perceptrons
- Multi-Layer Networks
- Deep Learning for Object Recognition
- Mind Body World (Sections 4.0 through 4.4)
- Convolution Problem
Summary of User Reviews
Discover the fascinating world of computational vision with the Mind and Machine course on Coursera. Students praise the comprehensive curriculum and engaging lectures.Key Aspect Users Liked About This Course
The comprehensive curriculum is highly praised by many users.Pros from User Reviews
- Engaging lectures that keep students interested
- In-depth coverage of the subject matter
- Challenging assignments that reinforce learning
- Highly knowledgeable and responsive instructors
- Excellent community of fellow learners
Cons from User Reviews
- Some students found the course to be too technical
- The pace of the course may be too fast for some learners
- Limited interaction with instructors
- Some assignments were too difficult or time-consuming
- No certification or accreditation offered
Master Computer Vision™ OpenCV4 in Python with Deep Learning
- 4.2
-
![]()
![]()
![]()
![]()
![]()
Master OpenCV4 like a pro while learning Dlib, Deep Learning Computer Vision (Keras, TensorFlow & Caffe) + 21 Projects! ====================================================== ======================================================...
Machine Learning and Data Science Hands-on with Python and R
- 3.9
-
![]()
![]()
![]()
![]()
![]()
Machine Learning, Statistics, Python, AI, Tensorflow, AWS, Deep Learning, R Programming, NLP, Bayesian, BI and much more Learn by doing. Full Lifetime Access....
Hands-On Natural Language Processing with Pytorch
- 3.1
-
![]()
![]()
![]()
![]()
![]()
Build smart language applications using Deep Learning You will build two complete real-world NLP applications throughout the course. This course uses Python 3....
3D Data Visualization for Science Communication
- 0.0
-
![]()
![]()
![]()
![]()
![]()
This course is an introduction to 3D scientific data visualization, with an emphasis on science communication and cinematic design for appealing to broad audiences....
