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Visual Recognition & Understanding

University at Buffalo and State University of New York via Coursera

This course may be unavailable.


This course immerses learners in deep learning, preparing them to solve computer vision problems. Learners plunge into the field of computer vision that deals with recognizing, identifying and understanding visual information from visual data, whether the information is from a single image or video sequence. Topics include object detection, face detection and recognition (using Adaboost and Eigenfaces), and the progression of deep learning techniques (CNN, AlexNet, REsNet, and Generative Models.)

This course is ideal for anyone curious about or interested in exploring the concepts of visual recognition and deep learning computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (free introductory tutorial: Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). It is highly recommended that learners take the “Deep Learning Onramp” course available at

Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes.

This is the fourth course in the Computer Vision specialization that lays the groundwork necessary for designing sophisticated vision applications. To learn more about the specialization, check out a video overview at

* A free license to install MATLAB for the duration of the course is available from MathWorks.


Introduction to Visual Recognition & Understanding
-This module provides an introduction to visual recognition and understanding in Computer Vision.

Early Techniques
-This module discusses optical character recognition, face detection, face recognition, and other early techniques used for visual recognition.

Deep Learning Overview
-In this module, we will discuss the history of Deep Learning, how it is used, and how it is revolutionizing the field of Computer Vision.

Deep Learning in Computer Vision: Applications
-This module provides information about the various applications of Deep Learning in Computer Vision.

Taught by

Radhakrishna Dasari and Junsong Yuan


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