10-601 SVMS

This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

Slides

• Slides in Powerpoint.

Readings

• Andrew Ng's notes on SVM optimization

Assignment

• None

What You Should Know Afterward

• The definitions of, and intuitions behind, these concepts:
  • The margin of a classifier relative to a dataset.
  • What a constrained optimization problem is.
  • The primal form of the SVM optimization problem.
  • The dual form of the SVM optimization problem.
  • What a support vector is.
  • What a kernel function is.
  • What slack variables are and why and when they are used in SVMs.
• How to explain the different parts (constraints, optimization criteria) of the primal and dual forms for the SVM.
• How the perceptron and SVM are similar and different.