Computational Vision (Coursera)

Computational Vision (Coursera)

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

What You Will 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.

Course 3 of 4 in the Mind and Machine Specialization.

Syllabus

WEEK 1
Introduction
This week we will explore some basic assumptions of a simple model of human vision.

WEEK 2
Edges, Depth, and Objects
This week we will explore models of higher-order tasks solved by the visual system.

WEEK 3
Mental Imagery
This week we will compare and contrast different perspectives of how mental imagery relates to the visual system.

WEEK 4
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.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Machine Learning for Accounting with Python (Coursera) Coursera
University of Illinois at Urbana-Champaign

Machine Learning for Accounting with Python (Coursera)

This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems.

Nov 16th 2026
5-12 Weeks
Deep Learning for Business (Coursera) Coursera
Yonsei University

Deep Learning for Business (Coursera)

Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers.

Nov 16th 2026
5-12 Weeks
Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera) Coursera
IBM

Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera)

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research.

Nov 9th 2026
4 Weeks
Machine Learning for Smart Beta (Coursera) Coursera
Sungkyunkwan University - SKKU

Machine Learning for Smart Beta (Coursera)

In this 4 week course, you will learn about Smart Beta products. Smart betas products have the characteristics of both passive investment(having predetermined rules) and active investments(allows for factor investment). We will walk through the creation mechanisms behind different smart beta products and recreate some of them using R programming.

Nov 16th 2026
4 Weeks
Machine Learning for Data Analysis (Coursera) Coursera
Wesleyan University

Machine Learning for Data Analysis (Coursera)

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering.

Nov 2nd 2026
4 Weeks
Supply Chain Analytics (Coursera) Coursera
IIT Roorkee

Supply Chain Analytics (Coursera)

Welcome to Supply Chain Analytics! In this course you will learn about advanced decision problems in Supply Chain Management and the application of optimisation formulations and their solutions to address them. The course has been designed to help you advance your career as business analysts, supply chain managers, and other similar roles by learning in-demand skills to increase efficiency, drive organisational growth, and make a positive business impact. The course also offers a good starting point to those with purely academic and research interests.

Nov 16th 2026
5-12 Weeks
Technologies and platforms for Artificial Intelligence (Coursera) Coursera
Politecnico di Milano

Technologies and platforms for Artificial Intelligence (Coursera)

This course will address the hardware technologies for machine and deep learning (from the units of an Internet-of-Things system to a large-scale data centers) and will explore the families of machine and deep learning platforms (libraries and frameworks) for the design and development of smart applications and systems.

Nov 9th 2026
4 Weeks
Preparing for the Google Cloud Professional Data Engineer Exam (Coursera) Coursera
Google Cloud

Preparing for the Google Cloud Professional Data Engineer Exam (Coursera)

From the course: "The best way to prepare for the exam is to be competent in the skills required of the job." This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don't know. And it helps you develop and practice skills required of practitioners who perform this job.

Nov 16th 2026
5-12 Weeks
Introduction to Embedded Machine Learning (Coursera) Coursera
Edge Impulse

Introduction to Embedded Machine Learning (Coursera)

Machine learning allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers. This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers.

Nov 8th 2026
3 Weeks
Practical Machine Learning on H2O (Coursera) Coursera
H2O.ai

Practical Machine Learning on H2O (Coursera)

In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms.

Nov 9th 2026
5-12 Weeks