Gradient Vectors
Gradient vectors (or “image gradients”) are one of the most fundamental concepts in computer vision; many vision algorithms involve computing gradient vectors for each pixel in an image.
SVM Tutorial - Part I
I found it really hard to get a basic understanding of Support Vector Machines. To learn how SVMs work, I ultimately went through Andrew Ng’s Machine Learning course (available freely from Stanford). I think the reason SVM tutorials are so challenging is that training an SVM is a complex optimization problem, which requires a lot of math and theory to explain.
Stanford Machine Learning - Lecture 6
This lecture covers:
Stanford Machine Learning - Lecture 5
This lecture covers the Gaussian Discriminant classifier and the Naive Bayes Classifier.
Stanford Machine Learning - Lecture 3
The third lecture covers the following topics (except where noted):