Same as 5900-14

Specialization: Standalone course
Instructor: Dr. Ioana Fleming, Instructor of Computer Science and Co-Associate Chair for Undergraduate Education
Prior knowledge needed: Basic calculus (differentiation and integration), linear algebra

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Learning Outcomes

  • Learners will be able to explain what Computer Vision is and give examples of Computer Vision tasks.​
  • Learners will be able to describe the process behind classic algorithmic solutions to Computer Vision tasks and explain their pros and cons.
  • Learners will be able to use hands-on modern machine learning tools and python libraries.

Course Content

Duration:  4h
In this module, you will learn about the field of Computer Vision. Computer Vision has the goal of extracting information from images. We will go over the major categories of tasks of Computer Vision and we will give examples of applications from each category. With the adoption of Machine Learning and Deep Learning techniques, we will look at how this has impacted the field of Computer Vision.

Duration:  4h
In this module, you will learn about classic Computer Vision tools and techniques. We will explore the convolution operation, linear filters, and algorithms for detecting image features.

Duration:  3h
In this module we will first review the challenges for object recognition in Classic Computer Vision. Then we will go through the steps of achieving object recognition and image classification in the Classic Computer Vision pipeline.

Duration:  5h
In this module we will compare how the image classification pipeline with neural networks differs than the one with classic computer vision tools. Then we will review the basic components of a neural network. We will conclude with a tutorial in Tensor flow where we will practice how to build, train and use a neural network for image classification predictions.

Duration:  7h
In this module we will learn about the components of Convolutional Neural Networks. We will study the parameters and hyperparameters that describe a deep network and explore their role in improving the accuracy of the deep learning models. We will conclude with a tutorial in Tensor Flow where we will practice building, training and using a deep neural network for image classification.

Duration:  2h
You will complete a peer reviewed final project worth 30% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.

Note: This page is periodically updated. Course information on the Coursera platform supersedes the information on this page. Click View on Coursera button above for the most up-to-date information.