Coursera

Computational Vision (Coursera)

Offered by University of Colorado Boulder,

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

Coursera

Johns Hopkins University

The Unix Workbench (Coursera)

Data Science

Unix forms a foundation that is often very helpful for accomplishing other goals you might have for you and your computer, whether that goal is running a business, writing a book, curing disease, or creating the next great app. The means to these goals are sometimes carried out by writing software. Software can’t be mined out of the ground, nor can software seeds be planted in spring to harvest by autumn. Software isn’t produced in factories on an assembly line. Software is a hand-made, often bespoke good. If a software developer is an artisan, then Unix is their workbench.

Sep 28th 2026

4 Weeks

Programming Unix Machine Learning

Coursera

IBM

Machine Learning Introduction for Everyone (Coursera)

Data Science

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more.

Oct 12th 2026

3 Weeks

ML Artificial Intelligence Machine Learning

Coursera

Board Infinity

The Rise of Generative AI (Coursera)

Data Science

This course offers a comprehensive exploration of the evolution of Generative AI, from its inception to its current state-of-the-art developments. Over seven video lectures, students will gain insights into key milestones, technologies, and the transformative impact of AI on various sectors.

Oct 12th 2026

1 Week

Artificial Intelligence Neural Networks AI

Coursera

University of California, San Diego

Machine Learning With Big Data (Coursera)

Statistics & Data Analysis Data Science

Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

Sep 28th 2026

5-12 Weeks

Algorithms Machine Learning Big Data

Coursera

University of Minnesota

Matrix Methods (Coursera)

Sci: Mathematics

Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction.

Oct 12th 2026

5-12 Weeks

Math Machine Learning Matrix

Coursera

University of Washington

Practical Predictive Analytics: Models and Methods (Coursera)

Statistics & Data Analysis Data Science

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Sep 28th 2026

4 Weeks

Machine Learning Models Methods

Coursera

Politecnico di Milano

Technologies and platforms for Artificial Intelligence (Coursera)

Robotics & Computer Vision

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.

Oct 12th 2026

4 Weeks

Artificial Intelligence Cloud Machine Learning

Coursera

IBM

Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera)

Data Science

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.

Oct 12th 2026

4 Weeks

Python Artificial Intelligence Prototyping

Coursera

University of Edinburgh

Data Science in Stratified Healthcare and Precision Medicine (Coursera)

Health & Society Statistics & Data Analysis

An increasing volume of data is becoming available in biomedicine and healthcare, from genomic data, to electronic patient records and data collected by wearable devices. Recent advances in data science are transforming the life sciences, leading to precision medicine and stratified healthcare. In this course, you will learn about some of the different types of data and computational methods involved in stratified healthcare and precision medicine.

Oct 5th 2026

5-12 Weeks

Medicine Healthcare Machine Learning

Coursera

University of Michigan

Data Augmented Technology Assisted Medical Decision Making (Coursera)

Data Science

Artificial intelligence (AI) and machine learning (ML) have the potential to increase diagnostic accuracy, decrease diagnostic errors, and improve patient outcomes. The Data Augmented, Technology Assisted Medical Decision Making (DATA-MD) course will teach you how to use AI to augment your diagnostic decision-making.

Oct 12th 2026

4 Weeks

Healthcare Artificial Intelligence Machine Learning

Coursera

Stanford University

Probabilistic Graphical Models 3: Learning (Coursera)

Statistics & Data Analysis Data Science

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

Sep 28th 2026

5-12 Weeks

Algorithms Machine Learning PGM

Coursera

University of Illinois at Urbana-Champaign

Text Retrieval and Search Engines (Coursera)

Statistics & Data Analysis Data Science

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text.

Sep 28th 2026

5-12 Weeks

Machine Learning Search Data Mining