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Course Description

Computer vision is one of the most quickly advancing domains of machine learning and perhaps one of the most successful applications of deep learning techniques and convolutional architectures. Image and video data has traditionally been unanalyzable accept for only the most lightweight at specific classifications primarily because of the size of data and its dimensionality. Distributed deep learning techniques have unlocked the information contained in this data precisely because they are designed to handle data of this scope. Applications such as automatic captioning, image search, robotics, and monitoring all show the power of deep learning on images. 

In this course, we will look at two deep learning techniques on image data: image classification and object detection. Using transfer learning and ImageNet, students will see how to quickly develop a multi-class image classifier on a custom data set. We will then extend the course to explore using MobileNet to recognize different objects inside a scene. Finally, we will discuss and experiment with facial recognition techniques and consider the implications of image classification and object detection in a real world context. 

 

Course Objectives

Upon successful completion of the course, students will be able to:

  • Understand the impact on privacy and security of scalable image analysis and machine vision.

  • Demonstrate the use of deep neural networks and transfer learning in image classification using ImageNet.

  • Explore use of MobileNet to implement object detection in still images and videos.

  • Experiment with Amazon Rekognition for facial recognition.

Notes

Enrollment in this course is open to all students and applies CEUs toward the Machine Learning or Ethics track.

Applies Towards the Following Certificates

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Type
Class
Days
Sa
Time
9:00AM to 4:00PM
Dates
Nov 06, 2021 to Nov 13, 2021
Schedule and Location
Contact Hours
12.0
Course Tuition
Tuition non-credit $828.00
Section Notes

Enrollment in this course is open to all students and applies CEUs toward the Machine Learning or Ethics track.

 

 

Welcome to the Flex Learning Experience - Real-time learning using live Zoom video conferencing— mirroring a more traditional classroom with regular interaction, - engaging activities, and the dynamic exploration of topics and concepts.

  • Dynamic exploration of topics, ideas and concepts with the instructor and students in the class
  • Interact regularly and frequently with your instructors and other students
  • Comparable level of accountability and engagement as classroom attendance
  • Lectures, discussions, and presentations occur at a specific hour
  • Face-to-face discussion, individual guidance, speed and immediacy to synchronous online learning
  • Immediate feedback - encouraging quick feedback on ideas, and support consensus and decision making
  • Pacing - encouraging students to keep up-to-date and provide a discipline to learning
  • Spontaneity - making it easy to add new ideas to the conversation, brainstorming or decision making
  • Familiarity - simulating a more traditional face-to-face environment

 

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