10-601B Perceptrons and Large Margin

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

Slides

Useful Additional Readings

  • The Perceptron Algorithm: Mitchell 4.4.1 & 4.1.2, Bishop 4.1.7
  • Support Vector Machines: Bishop 7.1, Murphy 14.5

What You Should Know Afterward

  • The difference between an on-line and batch algorithm.
  • The perceptron algorithm.
  • The importance of margins in machine learning.
  • 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.
    • What slack variables are and why and when they are used in SVMs.
  • How the perceptron and SVM are similar and different.
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