Material covered

• Classification based on class-conditional density
• Bayes-optimal classification, Bayes risk, discriminants
  
• Multivariate Gaussian (normal) distribution review, covariance
• Discriminants between Gaussian class-conditional densities
• Linear discriminant in the special case of equal covariance for all classes
• Comments on the special case of spherical Gaussian densities
• Discriminants are quadratic surfaces in case of diverse covariances
  
• Loss functions: 0/1 ("true"), perceptron, hinge, square
• Adding loss functions over instances in the model space
• The perceptron algorithm (proof of convergence deferred)
• Local regression using kernels
• Limiting case of impulse kernels supports large-margin principle