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Mathematics for Computer Vision

Course time: Duration: 60
Duration 60 Credits
Categories Skills, Computer Vision

Computer vision is a large field that integrates knowledge from many disciplines such as Image Processing, Computer Graphics, Pattern Identification, Machine Learning, Neural Networks, Algoritnms, Fuzzy Logic and Human Intelligence create. To understand the nature of this field students must have a solid foundation in computing, linear algebra, probability, statistics, geometry and algorithms. This course was created with the aim of equipping some of the most important and necessary mathematical tools used in computer vision. Our goal is to review mathematical techniques that have been widely used and prove to be useful in practical applications. The highlight of the course is the application aspect of mathematical techniques.The mathematical techniques that are accessed from a practical application perspective are then modeled and detailed programmed to help students not only understand the mathematical nature behind but also know how to apply it flexibly.

Part 1: Linear algebra

  • Vector and matrix
  • Math operations on the matrix
  • Eigenvalues and eigenvectors   
  • SVD and PCA
  • Application

Part 2: Statistical probability

  • Random variable
  • Joint probability
  • Marginalization
  • Conditional probability
  • Bayes Rules
  • Common probability distributions
  • The normal distribution
  • Maximum likelihood
  • Maximum a posteriori
  • Bayesian approach
  • Application

hSpace Team

hSpace Team

hSpace Team

Mathematics for Computer Vision