10-601 SVMS

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




  • 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.