Gaussian discriminant analysis

(1.5 hours to learn)


Gaussian discriminant analysis (GDA) is a generative model for classification where the distribution of each class is modeled as a multivariate Gaussian.


This concept has the prerequisites:


  • Derive the form of the decision boundary in the two-class case when the within-class covariance matrices are shared. In particular, show that it is always a hyperplane.
  • What do the decision regions look like when there are more than two classes?
  • Show that the decision boundary is quadratic when the covariance matrices are not shared between classes.
  • Optional: show that the decision boundary in the two-class case is equivalent to performing classification with linear least squares .

Core resources (read/watch one of the following)


The Elements of Statistical Learning
A graudate-level statistical learning textbook with a focus on frequentist methods.
Authors: Trevor Hastie,Robert Tibshirani,Jerome Friedman


Supplemental resources (the following are optional, but you may find them useful)


Stanford's Machine Learning lecture notes
Lecture notes for Stanford's machine learning course, aimed at graduate and advanced undergraduate students.
Author: Andrew Y. Ng

See also