Computer Vision

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About Course

This Computer Vision course provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, multi-view geometry including stereo, motion estimation, and tracking, and classification.

We’ll develop basic methods for applications that include finding known models in images, depth recovery from the stereo, camera calibration, image stabilization, automated alignment (e.g. panoramas), tracking, and action recognition. We focus less on the machine learning aspect of CV as that is really a classification theory best learned in an ML course.

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What Will You Learn?

  • Learn Image Classification
  • Learn Neural Networks
  • Learn CNN Architectures
  • Learn Detection and Segmentation
  • Learn Generative Models

Course Content

Computer Vision

  • Introduction to Convolutional Neural Networks for Visual Recognition
    00:00
  • Image Classification
    00:00
  • Loss Functions and Optimization
    00:00
  • Introduction to Neural Networks
    00:00
  • Convolutional Neural Networks
    00:00
  • Training Neural Networks
    00:00
  • Training Neural Networks
    00:00
  • Deep Learning Software
    00:00
  • Recurrent Neural Networks
    00:00
  • CNN Architectures
    00:00
  • Detection and Segmentation
    00:00
  • Visualizing and Understanding
    00:00
  • Generative Models
    00:00
  • Deep Reinforcement Learning
    00:00
  • Efficient Methods and Hardware for Deep Learning
    00:00
  • Adversarial Examples and Adversarial Training
    00:00

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